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Comprehensive Vulnerability Assessment of Urban Areas Using an Integration of Fuzzy Logic Functions: Case Study of Nasiriyah City in South Iraq

Comprehensive Vulnerability Assessment of Urban Areas Using an Integration of Fuzzy Logic... Article Comprehensive Vulnerability Assessment of Urban Areas Using an Integration of Fuzzy Logic Functions: Case Study of Nasiriyah City in South Iraq 1 , 2 , 3 1 2 Sadeq Khaleefah Hanoon * , Ahmad Fikri Abdullah , Helmi Z. M. Shafri and Aimrun Wayayok Civil Engineering Department, Faculty of Engineering, University Putra Malaysia, Serdang 43400, Malaysia; helmi@upm.edu.my Biological and Agricultural Engineering Department, Faculty of Engineering, University Putra Malaysia, Serdang 43400, Malaysia; ahmadfikri@upm.edu.my (A.F.A.); aimrun@upm.edu.my (A.W.) International Institute of Aquaculture and Aquatic Sciences (I-AQUAS), University Putra Malaysia, Port Dickson 70150, Malaysia * Correspondence: gs58154@student.upm.edu.my Abstract: Globally, urbanisation has been the most significant factor causing land use and land cover changes due to accelerated population growth and limited governmental regulation. Urban communities worldwide, particularly in Iraq, are on the frontline for dealing with threats associated with environmental degradation, climate change and social inequality. However, with respect to the effects of urbanization, most previous studies have overlooked ecological problems, and have disregarded strategic environmental assessment, which is an effective tool for ensuring sustainable development. This study aims to provide a comprehensive vulnerability assessment model for urban areas experiencing environmental degradation, rapid urbanisation and high population growth, to Citation: Hanoon, S.K.; Abdullah, help formulate policies for urban communities and to support sustainable livelihoods in Iraq and A.F.; Shafri, H.Z.M.; Wayayok, A. other developing countries. The proposed model was developed by integrating three functions Comprehensive Vulnerability of fuzzy logic: the fuzzy analytic hierarchy process, fuzzy linear membership and fuzzy overlay Assessment of Urban Areas Using an gamma. Application of the model showed that 11 neighbourhoods in the study area, and more than Integration of Fuzzy Logic Functions: 175,000 individuals, or 25% of the total population, were located in very high vulnerability regions. Case Study of Nasiriyah City in The proposed model offers a decision support system for allocating required financial resources and South Iraq. Earth 2022, 3, 699–732. efficiently implementing mitigation processes for the most vulnerable urban areas. https://doi.org/10.3390/ earth3020040 Keywords: vulnerability; urban; environment; infrastructure; uranium; MCDM; fuzzy; GIS Academic Editor: George D. Bathrellos Received: 14 May 2022 Accepted: 5 June 2022 1. Introduction Published: 8 June 2022 Globally, rapid urbanisation to meet the needs of uncontrolled population growth has led to several challenges, such as pollution, congested traffic, poor sustainability and Publisher’s Note: MDPI stays neutral negative impacts on the natural environment [1,2]. Cities have expanded at the expense with regard to jurisdictional claims in published maps and institutional affil- of green areas, leading to environmental degradation [3,4]. Rapid expansion has resulted iations. in the proliferation of many human activities that are difficult to manage; consequently, significant impacts on ecology and public health are likely to arise [5,6]. In the context of uncontrolled urban sprawl, a lack of financial resources and expertise, coupled with spatial marginalisation, has exposed entire urban areas to degradation risks [7]. Communities in Copyright: © 2022 by the authors. vulnerable areas face significant challenges, such as access to suitable public buildings, and Licensee MDPI, Basel, Switzerland. the availability of electricity, transportation, government education, healthcare and water This article is an open access article supply [8]. To respond to these challenges, current techniques need to be enhanced to cope distributed under the terms and with the complex changes occurring to the urban environment [9,10]. conditions of the Creative Commons Locally, given that Iraq is facing the consequences of long wars (1980 to 2003), military Attribution (CC BY) license (https:// action has strongly affected land use and land cover changes. The wars experienced creativecommons.org/licenses/by/ have contributed to environmental degradation, including through the transformation of 4.0/). Earth 2022, 3, 699–732. https://doi.org/10.3390/earth3020040 https://www.mdpi.com/journal/earth Earth 2022, 3 700 rivers, scorched earth exercises, the annihilation of animals and plants, oil spills, burning of petroleum wells and the use of chemical and biological weapons [11,12]. Moreover, non-traditional weapons used in Gulf Wars I and II have exposed Iraq’s environment to the harmful effects of the use of radioactive weapons [13]. Although a high level of environmental degradation and significant changes in Iraq’s environment have occurred, suitable measures to protect the environment are still lacking [14]. Urban communities have been on the frontline in dealing with the challenges of ecological degradation, urbanisation and the occurrence of different pandemics [15,16]. The sustainable development goals (SDGs) are goals for achieving long-term sus- tainability on Earth. With respect to these, in the short term, improvement in techniques that can provide sustainable solutions for urban areas that are high vulnerable should be a primary objective [17]. Mitigation and enhancement processes in urban areas must integrate approaches that match the SDGs and be applied to the most vulnerable areas as a priority [18–20]. Vulnerable areas should be prioritized when launching urban interven- tions, whilst urban sprawl should be simultaneously monitored and controlled [21]. There are a number of different approaches to the design of indicators that can comprehensively define, evaluate and address vulnerability, by integrating traditional data sources with modern Earth observation data [22–25]. Some researchers have proposed deprivation indices to measure deprivation in urban areas, such as the English indices of deprivation (IoD 2019) [26]. Others, such as Lynch and Mosbah (2017), have developed local indices to comprehensively measure sustainability [27]. Studies that have applied vulnerability theory to identify vulnerable urban areas have tended to be more comprehensive because they have sought to include a wide range of factors that can affect urban environments. Vulnerability theory has been applied by many researchers in the urban planning field. Hazell (2020) proposed ten criteria, divided into three major categories, namely, topographic, demographic and land cover attributes, to identify potentially vulnerable populations and to characterise desirable urban environment quality [28]. Ge et al. (2019) presented sixteen primary indicators for assessing social vulnerability, divided into four major categories: health inequality, cultural inequality, economic inequality and social inequality [29]. Ruá et al. (2021) defined four major domains, including the urban do- main (UD), building domain (BD), sociodemographic domain (SD) and the socioeconomic domain (SE), to evaluate vulnerable urban areas [30]. Similarly, Gerundo, Marra and de Salvatore (2020) utilized three dimensions (i.e., social domain, UD and BD) to construct a composite vulnerability index for describing vulnerable urban areas [7]. In another study, conducted by Gerundo et al. (2020), the authors proposed a set of mitigation indicators for three major dimensions, the social domain, BD and UD, as useful tools for assessing vulnerability [31]. However, most models used in this context overlook ecological problems that are associated with urbanisation and disregard strategic environmental assessment (SEA), which is an effective technique for assessing environmental damage due to human activity to ensure that urban development is sustainable [32–34]. Therefore, the current study seeks to bridge this gap by presenting a comprehensive vulnerability assessment technique that can effectively define vulnerable urban areas and monitor urban sprawl. The approach is relevant to the environmental impact assessment of polluting activities in urban areas as a significant part of a total vulnerability evaluation. Many techniques are available for evaluating vulnerability in urban areas, including multi-criteria decision analysis (MCDA) for assessing multiple factors that contribute to the complexity of the urban fabric [35,36]. The integration of MCDA into a geographic information system (GIS) is commonly used to resolve various complicated spatial prob- lems. Furthermore, available remote-sensing (RS) datasets and expert opinion make such integration more efficient for supporting the decision-making process [37,38]. The approach enables the combining of data derived from different geographical factors into a single measurement index [39], to assess the reality of the situation and identify implications for ecological sustainability [19,35,40]. Although more than 15 different MCDA methods are currently available, the most notable is the analytic hierarchy process (AHP) [41,42]. How- Earth 2022, 3 701 ever, AHP applies crisp values, and its results are accompanied by uncertainty; thus, fuzzy AHP (FAHP) has emerged as an upgraded version of AHP that reflects human reasoning processes [43]. Indicators with multiple levels and weighted importance that result from FAHP can be compared to support decision-makers in defining optimal alternatives and indicators [2]. Whilst the AHP technique provides satisfactory results, FAHP deals with uncertainty values that are associated with vulnerability indicators [44]. Fuzzy logic (FL) is the most effective application of spatial analysis in the urban planning field which has been extensively improved as a significant function of GIS [45,46]. It can evaluate the different degrees of membership for complex topics associated with uncertainty, such as vulnerability indicators [7]. FL includes several types of functions. Fuzzy linear membership (FLM) is one of these functions; it can be operated with MCDA to standardise criteria to make wise decisions and convert various parameters into fuzzy values between 0 and 1 [47,48]. The fuzzy overlay (FO) function is applied when analysing the effects of various factors related to many sets in the multi-criteria overlay technique. The FO function analyses the relationships between the sub-criteria of multiple major criterion sets [49]. Furthermore, some significant functions are involved in FO that allow combining fuzzy membership values for diverse variables by performing a cell-by-cell overlay process [50,51]. FO gamma (FOG) is the most significant function that results from multiplying a fuzzy product value by a fuzzy sum value. Both values are raised to the power of gamma. FOG makes an adjustment between the increasing fuzzy sum value and the diminishing effect of the fuzzy product value [52,53]. The current study presents a new approach for the comprehensive vulnerability as- sessment of urban areas. The proposed approach takes advantage of effective fuzzy logic functions to overcome uncertainty in the classification and combination of vulnerability indicators, which represents a significant strategy for making sensitive decisions associated with human life. It was used to integrate (FAHP), (FLM) and (FOG) to derive a comprehen- sive vulnerability indicator for Nasiriyah City in Iraq. The comprehensive vulnerability indicator is an algebraic product of environmental vulnerability with urban vulnerability, building vulnerability and social vulnerability, produced in accordance with vulnerability theory to define vulnerable urban areas. The new approach enables building of a robust database and provision of relevant guidance for comprehensive vulnerability assessment, serving as an improved decision support system for determining priority intervention sites within complicated urban areas. In addition, the system enables optimisation of public spending for mitigating vulnerability given that local authorities responsible for city services frequently have insufficient financial resources. This approach can be applied to enhance policies formulated for urban communities and help build sustainable livelihoods in all regions of Iraq and other developing countries. 2. Vulnerability Indicators Vulnerability emerges from environmental, physical, economic, and social problems in urban areas. This term is used to describe a reduced capacity to adapt to, resist and recuperate from risks [54,55]. Thus, urban vulnerability can be described as a situation that arises from the combination of multiple disadvantageous factors leading to challenging circumstances that it is difficult for an urban community to overcome [56,57]. The recogni- tion and measurement of these factors is essential before implementing plans to mitigate vulnerability. The most suitable measurement approach is based on vulnerability theory; it combines different vulnerability indicators, such as social, urban, building and envi- ronmental indices, into a single indicator to represent the situation to support mitigation planning. This method enables diagnosis of urban problems and identification of solutions without requiring substantial data collection [58]. Collecting data associated with many indicators is extremely difficult; hence, the vulnerability assessment process can be accomplished by focusing on different indicators dependent on local conditions or data availability [59]. A comprehensive vulnerability assessment based on vulnerability theory was performed for the study area (Nasiriyah City, Earth 2022, 3 702 Iraq) to define vulnerable urban areas by measuring multiple criteria that are pertinent to urban communities. A total of twelve sub-criteria were selected based on literature review, local urban and environmental indicators and data availability. The Delphi technique was applied to confirm the suitability of criteria for the vulnerability indicators. The sub-criteria were categorised into four major domains: environment, urban, building and social. 2.1. Environment Domain Environmental vulnerability indicators estimate the capability of urban communities to recover from possible risks of pollution arising from several pollution sources; this capability depends primarily on the healthiness, integrity and organisational level of a com- munity [60]. Pollution sources can be classified into two major groups: point and non-point sources of pollution. The locations of point sources of pollution, such as industrial activities, can be determined. However, point-source pollution in Iraqi cities mostly originates from distributed pollution sites, such as oil industry operations, power stations, landfill sites, brick factories and wastewater treatment plants (WWTPs). The oil industry sector is a key environmental pollution source in Iraq; it releases polluting gases that affect residential neighbourhoods close to or in buffer zones [61]. WWTPs can be hotspots for the spread of antibiotic-resistant pathogens with significant effects on water ecosystems. In addition, weapon storage sites in which depleted uranium was used during the wars have continued to be tremendously harmful to public health and to Iraq’s environment since the conflict period. By contrast, non-point sources of pollution are more difficult to determine and require more effort to control. Many sites release polluting materials simultaneously [62]. The current study applied local environmental standards (specifically, number 3-2011) that have been adopted by the Iraqi Ministry of Environment. These standards determine buffer zones with different radii based on the degree of pollution. Residential neighbour- hoods located inside buffer zones are considered as urban areas exposed to pollution risks. The local environmental standards classify point-source pollution into three categories, as described below. 2.1.1. Class A: High-Polluting Projects This category includes many polluting projects, such as oil refineries, iron industries, WWTPs, brick factories, thermal power plants and landfill sites. Table 1 lists some types of high-polluting projects with their respective buffer zones based on the classification of local environmental indicators in Iraq. Table 1. Samples of high-polluting projects (Class A). Activity Types Buffer Zone Radius (km) Dangerous landfill 15 Oil refinery 10 Gas plant 10 Aluminium and cable factories 10 Thermal power station 5 Iron plant 5 Brick factory 5 Protein feed factory 3 Asphalt plant 5 Landfill 2 WWTPs 2 Earth 2022, 3 703 2.1.2. Class B: Moderately Polluting Projects This class involves polluting projects that affect the environment less than Class A projects, such as the poultry industry, plastic manufacturers, gas turbine power plants, concrete manufacturers, flour mills and date canning factories. Table 2 lists several types of moderately polluting projects with their respective buffer zone radii based on Iraqi environment indicators. Table 2. Examples of moderately polluting projects (Class B) according to Iraqi environment standards. Activity Types Buffer Zone Radius (m) Flour mill processing plant 1000 Gas power plant 1000 Wire plant 1000 Poultry industry 1000 Poultry slaughter 1000 Sandwich panel industry 1000 Woolen textile factory 500 Concert plant 500 Plastic and paint plant 500 2.1.3. Class C: Low-Polluting Projects This class includes polluting projects that affect the environment less than Class B projects, such as wastewater pumping stations, oil stores and industrial complexes. Table 3 presents some types of low-polluting projects and their respective buffer zones based on Iraqi environmental indicators. Table 3. Examples of low-polluting projects (Class C) according to Iraqi environment standards. Activity Types Buffer Zone Radius (m) Site of oil stores 500 Vehicle industrial complex 500 Pumping station of wastewater 20 2.1.4. Effects of Weapons and War A number of major international reports have confirmed that unconventional weapons used during the Gulf wars (1991–2003) were among the primary reasons for an increase in cancerous diseases in Iraq [62,63]. Large amounts of depleted uranium (DU) were fired during the Iraq wars [64]. DU has increased environmental pollution dangerously due to effects that appeared after the wars [65,66]. About 300 tons of DU were fired in the first Gulf war and about 1700 tons were fired during the 2003 war [67]. Reports have confirmed that radioactive materials (DU) that were routinely stored in military bases located close to Nasiriyah City, i.e., the study area, have leaked into the environment [68]. The most danger- ous site (the Khamisiyah site) in which chemical weapons and DU were stored is located 17 km from the border of the study area [69,70]. Radioactive emissions have permeated into the surroundings, and, as a result, people have been exposed to their dangerous effects [13]. In the current study, the effect of weapon use was defined as a polluting factor within the environmental domain. Thus, an evaluation of the effects of weapons on the environment in the study area was performed according to Iraqi environment standards, which contributed to determining the buffer zone for dangerous landfills, detailed in Table 1. Earth 2022, 3 704 2.2. Building Domain Statistical analysis conducted using quantitative and qualitative indices has shown that the vulnerability of an urban environment is primarily linked to financial resources, authority policies and city size [71]. As urbanisation continues to accelerate due to rapid population growth in Iraq, the problems arising in urban communities are becoming more complex. City authorities do not have sufficient financial and technical capabilities to provide all city neighbourhoods with basic infrastructure, such as paved streets and sewage networks. The Nasiriyah City administration is unable to control the rapid sprawl, and informal settlements involving illegal construction have continuously increased to accommodate the accelerating population growth. The informal settlements are a source of environmental pollution and a reason for the increasing number of vulnerable urban areas in Iraq. The most dangerous consequence of informal construction is the lack of proper services, such as construction of unpaved roads, which are considered a significant source of dust pollution, and the lack of public sewer and solid waste treatment systems [72]. Urban areas can be defined as vulnerable areas based on construction characteristics, particularly the infrastructure, shape and density of a settlement and its location [23]. Two sub-criteria were adopted in the current study to define vulnerable neighbourhoods within the building domain: (1) the ratio of informal settlements, and (2) the lack of infrastructure at the neighbourhood scale. 2.3. Urban Domain The most important impact factors in urban planning are urban density, population density and green public spaces, which are directly or indirectly related to vulnerability in- dicators. The well-being of urban communities is central to consideration of how the urban landscape, building density and open spaces can be utilized to address urban sprawl [73]. The integration of population and dwelling density maps enables the identification of neighbourhoods with high population density and low basic services in which mitigation interventions are urgently required [10]. In the current study, vulnerability indicators, including population density, dwelling density and green area, were classified under the urban domain to define vulnerable urban areas. 2.4. Social Domain A body of previous research has defined social vulnerability as the vulnerability of people or neighbourhoods. Social vulnerability, as a concept, has been used to characterise the capacity to control hazards and their consequences for urban communities, social groups and families [60]. Social vulnerability assessment has focused on understanding the factors associ- ated with social inequality that increase vulnerability at family and community scales [74]. The provision of health care and educational services and the availability of job opportunities are significant social indicators that can indicate the social vulnerability of urban communi- ties [9,75,76]. Therefore, data for three criteria, namely, health care services, education services and unemployment ratio, were collected in the current study to define social vulnerability indicators consistent with the local urban planning indicators of Iraq (Table 4). Table 4. Iraqi urban planning standards that refer to access distance to health care centres and schools, along with the size of social services required based on the number of people. Maximum Access Distance Facility from Dwellings to Facility Number of Units/Population (m) Nursery 300 1 per 2400–3600 capita Kindergarten 300 1 per 2400–3600 capita Primary school 500 1 per 2400–3600 capita Intermediate school 500 1 per 9600–14,400 capita Secondary school 800 1 per 9600–14,400 capita Earth 2022, 3 705 Earth 2022, 3, 7 Table 4. Cont. Maximum Access Distance Intermediate school 500 1 per 9600–14400 capita Facility from Dwellings to Facility Number of Units/Population Secondary school 800 1 per 9600–14400 capita (m) Health centre 800 1 per 9600–14400 capita Health centre 800 1 per 9600–14,400 capita Open space / 6.25 m per capita Open space / 6.25 m per capita Streets / 11.6% to 26% from total area Streets / 11.6% to 26% from total area Population density per / 250 persons per hectare (p/h) Population density per hectare / 250 persons per hectare (p/h) hectare Housing density / 42 dwellings per hectare (d/h) Housing density / 42 dwellings per hectare (d/h) 3. Method 3. Method 3.1. Study Area 3.1. Study Area Nasiriyah City was selected as the study area for this research. It represents Iraqi Nasiriyah City was selected as the study area for this research. It represents Iraqi cities because Iraq’s urban characteristics are quite similar across the whole area. Nasiri- cities because Iraq’s urban characteristics are quite similar across the whole area. Nasiriyah yah City is located along the banks of the Euphrates River, between latitudes 31°90′00″ N 0 00 City is located along the banks of the Euphrates River, between latitudes 31 90 00 N and and 30°50′00″ N, and between longitudes 46°00′00″ E and 46°20′00″ E, as shown in Figure 0 00  0 00  0 00 30 50 00 N, and between longitudes 46 00 00 E and 46 20 00 E, as shown in Figure 1. The 1. The average elevation is about 4 m above mean sea level, and its area is more than 46,000 average elevation is about 4 m above mean sea level, and its area is more than 46,000 hectares. hectares. The total population of over 700,000 people (based on the 2021 local census) cur- The total population of over 700,000 people (based on the 2021 local census) currently occupy rently occupy 92 neighbourhoods. The study area covered the Ur archaeological site (4000 92 neighbourhoods. The study area covered the Ur archaeological site (4000 BCE), as shown BCE), as shown in Figure 1. Nasiriyah City is the capital of Dhi Qar Province. The city has in Figure 1. Nasiriyah City is the capital of Dhi Qar Province. The city has suffered from the suffered from the severe effects of wars. The most dangerous site, i.e., the Khamisiyah site, severe effects of wars. The most dangerous site, i.e., the Khamisiyah site, where chemical where chemical weapons and depleted uranium (DU) were used, is located about 17 km weapons and depleted uranium (DU) were used, is located about 17 km from its borders. from its borders. Figure 2 shows the location of this site [69,70]. This dangerous site has Figure 2 shows the location of this site [69,70]. This dangerous site has become closer to the become closer to the city settlements due to rapid urban sprawl, high population growth, city settlements due to rapid urban sprawl, high population growth, migration towards the migration towards the city and poor urban planning, resulting in the establishment of city and poor urban planning, resulting in the establishment of large informal settlements in large informal settlements in the study area the study area. Figure 1. Location of Nasiriyah City. Upper left: location of Iraq on the world map. Bottom left: location of Nasiriyah City on the Iraq map. Right: map of Nasiriyah City showing the location of the ancient city of Ur (4000 BC). The brown area comprises 92 occupied neighbourhoods. Earth 2022, 3, 8 Figure 1. Location of Nasiriyah City. Upper left: location of Iraq on the world map. Bottom left: location of Nasiriyah City on the Iraq map. Right: map of Nasiriyah City showing the location of Earth 2022, 3 706 the ancient city of Ur (4000 BC). The brown area comprises 92 occupied neighbourhoods. Figure 2. Khamisiyah site and boundary of Nasiriyah City. The three pictures on the right show a Figure 2. Khamisiyah site and boundary of Nasiriyah City. The three pictures on the right show a close-up view of the Khamisiyah site. close-up view of the Khamisiyah site. 3.2. Data Collection 3.2. Data Collection In this study, 12 dataset layers were collected to identify the vulnerable urban areas in In this study, 12 dataset layers were collected to identify the vulnerable urban areas Nasiriyah City. These layers were as follows: high-polluting sources, moderately polluting in Nasiriyah City. These layers were as follows: high-polluting sources, moderately pol- sources, low-polluting sources, DU landfill, informal settlement rate, lack of infrastructure, luting sources, low-polluting sources, DU landfill, informal settlement rate, lack of infra- housing density, population density, green space, health care service size, education service structure, housing density, population density, green space, health care service size, edu- size and unemployment rate. They were categorised into four major domains: environment, cation service size and unemployment rate. They were categorised into four major do- building, urban and social, as shown in Figure 3. In addition, land surveying was conducted mains: environment, building, urban and social, as shown in Figure 3. In addition, land to obtain accurate results by utilising global positioning system (GPS) instruments. Table 5 surveying was conducted to obtain accurate results by utilising global positioning system describes the datasets used in this study. (GPS) instruments. Table 5 describes the datasets used in this study. Table 5. Types, description and accuracy of the data used in this study. Table 5. Types, description and accuracy of the data used in this study. No. Data Description Source Accuracy No. Data Description Source Accuracy It was used to classify the land It was used to classify the land cover European Union’s Earth European Union’s Sentinel 2 cover of the study area and extract Sentinel 2 of the study area and extract green 1 observation programme 10 m 1 image, October 2021 green space (sub-criteria of the Earth observation pro- 10 m (Copernicus) image, October 2021 space (sub-criteria of the urban do- urban domain). gramme (Copernicus) main). The images were utilised to The images were utilised to validate validate land use classes and the Pléiades 1, product name: Pléiades 1, product name: Iraqi General Survey land use classes and the boundary of Iraqi General Survey 2 boundary of old neighbourhoods 0.50 m ORTHO, 2014 Authority 2 ORTHO, 0.50 m and to increase the resolution of old neighbourhoods and to increase Authority image classification. the resolution of image classification. The data were analysed spatially The data were analysed spatially to Office of the Munici- Land use—streets, districts to classify land use classes, street 3 classify land use classes, street case pality of Nasiriyah 2 m Land use—streets, districts case (asphalt or dusty) and Office of the Municipality and water networks, 2021 3 2 m (asphalt or dusty) and wastewater City, Iraq and water networks, 2021 wastewater discharge systems of Nasiriyah City, Iraq (sewage network systems or home septic tanks). Earth 2022, 3 707 Table 5. Cont. No. Data Description Source Accuracy The shape files were analysed to Master plan of compare actual land use with the Office of Urban Planning, 4 2 m Nasiriyah City master plan of the city based on Nasiriyah City, Iraq urban planning indicators. They were manipulated spatially to determine the locations of Pipeline wastewater, polluted sources (WWTPs) and manhole sewages, pump Office of Sewage 5 pump stations of wastewater. 2 m stations and water Department in Dhi Qar Spatial analysis of infrastructure treatment stations (WTSs) distribution in the city was conducted. They were treated spatially and Poultry sites, protein then entered within the Agriculture Directorate of 6 5 m factories and animal feeds sub-criteria of the Dhi Qar, Iraq environment domain. They were analysed spatially and listed under point-source pollution Dhi Qar Investments 7 Polluted industrial projects 5 m (sub-criteria of the Office (Iraq) environment domain). They were manipulated and integrated with spatial data and Dhi Qar Environment 8 Polluted sites (2021) 1 m then organised under Office (Iraq) point-source pollution. The data were analysed spatially and then compared with urban Health care centres and Ministry of Health 9 planning indicators before being 2 m hospitals (2021) (Dhi Qar office, Iraq) entered into the sub-criteria of the social domain. The same processes in Item (9) Ministry of Education 10 Schools (2021) 2 m were performed. (Dhi Qar office, Iraq) Data were entered into the Iraqi Ministry of Planning, Neighbourhood 11 Unemployment rate (2021) sub-criteria of the social domain. Department of Statistics scale They were converted into raster form and then utilised to validate 1/25,000 Office of Urban Planning, 12 Paper maps (2020) the image classification and spatial 1/10,000 Nasiriyah City, Iraq distribution pattern digitisation of 1/2500 missed geographic features. Population housing Data were entered as sub-criteria Ministry of Neighbourhood census (2021) of the urban domain. Planning/Statistics Office scale The work was required to validate Site survey using data, increase the resolution of the 14 Author 2 m GPS (2022) geographic features of locations and complete missing data. They were used for the Site survey using drone 15 digitalisation of informal Author 2 m images (February 2021) settlements. 3.3. GIS Database Design and Management A geodatabase was designed by applying various GIS operations. These procedures were applied to vector data versus raster data, which differed in structure. Raster data contain equal-sized cells that form a continuous surface. Vector data comprise polygons, lines and points that form distinct geographic features on Earth. In addition, spatial and Earth 2022, 3, 10 Earth 2022, 3 708 lines and points that form distinct geographic features on Earth. In addition, spatial and textual data were integrated into the geodatabase. Subsequently, the sub-criteria relevant textual data were integrated into the geodatabase. Subsequently, the sub-criteria relevant to vulnerability were extracted and then categorised into four major criteria: environment, to vulnerability were extracted and then categorised into four major criteria: environment, building, urban and social. Figure 3 shows the layers of the sub-criteria that were required building, urban and social. Figure 3 shows the layers of the sub-criteria that were required for running the MCDA to define vulnerable urban areas. for running the MCDA to define vulnerable urban areas. Figure 3. Figure 3.F Flowchart lowchart of ofdata dataco collection llection and and cla classification ssification of of the criter the criteria ia and and s sub-criteria. ub-criteria. 3.4. Delphi Technique 3.4. Delphi Technique The criteria and sub-criteria that were defined based on the literature review were The criteria and sub-criteria that were defined based on the literature review were reviewed by an expert panel using the Delphi method to confirm the criteria that were the reviewed by an expert panel using the Delphi method to confirm the criteria that were most relevant to the vulnerability indicators. Delphi is an expert judgment technique in the most relevant to the vulnerability indicators. Delphi is an expert judgment technique which a in whichgroup of well-kno a group of well-known wn experts in a spec experts in a specific ific field expre field expr ss t ess heir opin their opinions ions durin during g a series of discussions by following a prepared questionnaire to arrive at the group’s opin- a series of discussions by following a prepared questionnaire to arrive at the group’s ions opinions about about a specif a specific ic issue issue [77]. An expe [77]. Anrt expert panel w panel as care was fu car llyefully selected. It selected. cons It isconsisted ted of 22 qu ofalified 22 qualified experts, expe sixrts, expe six rts from experts the e from nvi the ronment doma environmentin domain, , ten experts ten experts from the from urba the n plannin urban planning g department department and six experts fr and six experts om the con from s the truction constr domain. The uction domain. expert The s p experts artici- participated in multiple meetings with the purpose of integrating viewpoints into a group pated in multiple meetings with the purpose of integrating viewpoints into a group con- consensus. After each round, the answers were summarised and transferred to the experts. sensus. After each round, the answers were summarised and transferred to the experts. The experts were allowed to modify their responses in the next rounds, depending on how The experts were allowed to modify their responses in the next rounds, depending on they analysed the group opinion. The result of this method was that nearly all the criteria how they analysed the group opinion. The result of this method was that nearly all the and sub-criteria were approved as relevant to urban vulnerability indicators. Figure 3 criteria and sub-criteria were approved as relevant to urban vulnerability indicators. Fig- shows the major criteria and sub-criteria endorsed by the expert panel. ure 3 shows the major criteria and sub-criteria endorsed by the expert panel. 3.5. Spatial Analysis Processes 3.5. Spatial Analysis Processes After the collected data were organised into four primary datasets (urban, building, After the collected data were organised into four primary datasets (urban, building, social and environment), two types of spatial analysis technique (continuous and discrete) social and environment), two types of spatial analysis technique (continuous and discrete) were performed according to the type of data before a weighted linear combination func- were performed according to the type of data before a weighted linear combination func- tion (WLC) was applied to produce vulnerability indicators for each domain, as shown tion (WLC) was applied to produce vulnerability indicators for each domain, as shown in in Figure 4. Figure 4. Earth 2022, 3, 11 Earth 2022, 3 709 Figure 4. Flowchart of the methods used to produce vulnerability indicators: environment (Ve), Figure 4. Flowchart of the methods used to produce vulnerability indicators: environment (Ve), ur- urban (Vu), building (Vb) and social (Vs). ban (Vu), building (Vb) and social (Vs). 3.5.1. Spatial Analysis of Continuous Data 3.5.1. Spatial Analysis of Continuous Data In this study, data belonging to the environment domain was continuous. Given that In this study, data belonging to the environment domain was continuous. Given that all polluting projects and the DU landfill represented point sources of pollution (PSPs), their all polluting projects and the DU landfill represented point sources of pollution (PSPs), effects could be continuous across the study area to a different degree based on distance their effects could be continuous across the study area to a different degree based on dis- from the source. Therefore, two sequential operations, namely, Euclidean distance and tance from th FLM, were conducted e source. The befor refore e the, two se sub-criteria quen wer tial operations, namely e weighted with values , Euc obtained lidean distanc from e FAHP. Then, an FO analysis was performed on the major criteria to obtain the final fuzzy and FLM, were conducted before the sub-criteria were weighted with values obtained map of total vulnerability. from FAHP. Then, an FO analysis was performed on the major criteria to obtain the final fuzzy map of total vulnerability. 1. Euclidean distance function 1. Euclidean distance function Euclidean distance is a spatial analysis function available in the GIS environment. It uses the Pythagorean theorem to calculate the Euclidean distance to the closest source Euclidean distance is a spatial analysis function available in the GIS environment. It for each cell based on Formula (1). Through this function, the vector layer dataset that uses the Pythagorean theorem to calculate the Euclidean distance to the closest source for belonged to the environment domain was converted into raster form that indicated the each cell based on Formula (1). Through this function, the vector layer dataset that be- existing distances from the pollution source to the remaining buffer area. longed to the environment domain was converted into raster form that indicated the ex- isting distances from the pollution source to the remaining buffer area. h i 2 2 d = (X2 X1) + (y2 y1) (1) ( ) ( ) (1) 𝑑 = 𝑋2 − 𝑋1 + 𝑦2 − 1 𝑦 where d represents the distance between a pollution source and the remaining points. where d represents the distance between a pollution source and the remaining points. 2. Fuzzification 2. Fuzzification Whilst FL emulates human logic by using artificial intelligence (AI) techniques, only Whilst FL emulates human logic by using artificial intelligence (AI) techniques, only two options are restricted in the Boolean logic (BL) of computers: 0 or 1 [78]. FL allows for a two options are restricted in the Boolean logic (BL) of computers: 0 or 1 [78]. FL allows for degree of contribution to the reaction, represented by a membership function [49]. Although a degree of contribution to the reaction, represented by a membership function [49]. Alt- classical theory is founded on crisp sets, according to which each indicator belongs to a hough classical theory is founded on crisp sets, according to which each indicator belongs quite-determined class, FL access evaluates the different degrees of membership of each to a quite-determined class, FL access evaluates the different degrees of membership of indicator to various classes. This approach has been used in contemporary research by each indicator to various classes. This approach has been used in contemporary research examining a complex topic affected by uncertainty, such as vulnerability to climate change by examining a complex topic affected by uncertainty, such as vulnerability to climate and urbanisation [7]. A negative FLM function was used to standardise the sub-criteria of change and urbanisation [7]. A negative FLM function was used to standardise the sub- the environment domain (effects of Classes A, B and C and DU) into fuzzy values between criteria of the environment domain (effects of Classes A, B and C and DU) into fuzzy val- ues between 0 and 1 based on Formula (2). Figure 5 illustrates negative and positive FLM functions, i.e., Formula (3). Earth 2022, 3, 12 Earth 2022, 3 710 1 𝑥 < 0 0 and 1 based on Formula (2). Figure 5 illustrates negative and positive FLM functions, i.e., (𝑀𝑎𝑥 − 𝑥) Formula (3). ( ) 𝑚𝑖𝑛 < 𝑥 < 𝑚𝑎 𝑓 𝑥 = 8 (2) (𝑀𝑎𝑥 − 𝑚) 1 x < 0 0 𝑖𝑓 𝑥 > 𝑚𝑎𝑥 (Max x) f (x) = min < x < ma (2) > (Max min) 1 𝑜𝑟 0 𝑥 > 𝑚𝑎𝑥 0 i f x > max 0 𝑜𝑟 1 𝑥 < 𝑚𝑖𝑛 ( ) 𝑓 𝑥 = (3) 8 (𝑋 − ) 1 or 0 x 𝑚𝑖> 𝑛 <m 𝑥a x < 𝑚𝑎 (𝑀𝑎𝑥 − 𝑖𝑛𝑀 ) 0 or 1 x < min f (x) = (3) (X Min) : min < x < ma (Max Min) Figure 5. The yellow line represents a negative FLM function based on Formula (2), whilst the blue line represents a positive FLM function based on Formula (3). 3.5.2. Methods for Processing and Analysing Discrete Data Figure 5. The yellow line represents a negative FLM function based on Formula (2), whilst the blue line represents a positive FLM function based on Formula (3). Data belonging to the urban, building and social domains are discrete data. The collected data were distributed inside the boundaries of neighbourhoods based on actual 3.5.2. Methods for Processing and Analysing Discrete Data quantities. The collected data related to the sub-criteria were manipulated, and spatial analysis Data bel was onging to the urb performed within an, buil the boundaries ding and soc of the ial do neighbour mains are hoods. discrete d Therefor ata. Th e, discr e col- ete lected data wer date a w converted ere distr into ibute raster d insid form e the using boundar the rasteri ies o sation f neighbo function urhoods b and then asenormalisation d on actual was applied to standardise the original values of the sub-criteria. quantities. The collected data related to the sub-criteria were manipulated, and spatial analysis was performed within the boundaries of the neighbourhoods. Therefore, discrete 1. Rasterisation data were converted into raster form using the rasterisation function and then normalisa- Eight raster layers were generated from the shape file form of the sub-criteria belonging tion was applied to standardise the original values of the sub-criteria. to the three domains (building, urban and social) based on specific values, as shown 1. Rasterisation in Figure 6. Eight raster layers were generated from the shape file form of the sub-criteria belong- ing to the three domains (building, urban and social) based on specific values, as shown in Figure 6. 𝑀𝑖𝑛 𝑖𝑛 Earth 2022, 3 711 Earth 2022, 3, 13 Earth 2022, 3, 13 Figure 6. Conversion of a shape file into raster form using the rasterisation tool. Figure 6. Conversion of a shape file into raster form using the rasterisation tool. Figure 6. Conversion of a shape file into raster form using the rasterisation tool. 2. Normalisation 2. Normalisation 2. Normalisation In quantitative studies with various data sources, such as the current study, stand- In quantitative studies with various data sources, such as the current study, standard- In quantitative studies with various data sources, such as the current study, stand- isation ardisatis ior n equir is reed quito red make to mmeaningful ake meaning comparisons ful compariso on nsthe on t basis he ba of sis values of valu measur es meaed sur in ed ardisation is required to make meaningful comparisons on the basis of values measured dif in di ferent fferent units un [79 its [ ]. Ther 79]. Th efor er e, efore normalising , normalis the ing original the original v values a of lues theof the gener generated raster ated raster from in different units [79]. Therefore, normalising the original values of the generated raster the from t previous he prev step ious was step wa necessary s necess toary permit to permit expressive exprescomparisons. sive comparisons. A All original ll origivalues nal val- from the previous step was necessary to permit expressive comparisons. All original val- were converted between 0 and 1 in this step based on the linear interpolation equation, i.e., ues were converted between 0 and 1 in this step based on the linear interpolation equation, ues were converted between 0 and 1 in this step based on the linear interpolation equation, Formula i.e., Form (4). ula Whilst (4). Wh number ilst number one ind one indicatesicat extr es eme extreme vulner vulnerability abilit , zero y, ze indicates ro indiminimum cates mini- i.e., Formula (4). Whilst number one indicates extreme vulnerability, zero indicates mini- vulnerability. Therefore, the increment in value is associated with an increase in potential mum vulnerability. Therefore, the increment in value is associated with an increase in mum vulnerability. Therefore, the increment in value is associated with an increase in vulnerability for a specific sub-criterion, as shown in Figure 7. potential vulnerability for a specific sub-criterion, as shown in Figure 7. potential vulnerability for a specific sub-criterion, as shown in Figure 7. 𝑥𝑖 − 𝑥𝑖. 𝑚𝑖𝑛𝑚𝑢𝑚 xi xi.minmum 𝑦𝑖 = (4) 𝑥𝑖 − 𝑥𝑖. 𝑚𝑖𝑛𝑚𝑢𝑚 yi = (4) 𝑥𝑖. 𝑢𝑚𝑚𝑎𝑥𝑖𝑚 − 𝑥𝑖. 𝑚𝑚𝑖𝑛𝑖𝑚𝑢 (4) 𝑦𝑖 = xi.maximum xi.minimum 𝑥𝑖. 𝑢𝑚𝑚𝑎𝑥𝑖𝑚 − 𝑥𝑖. 𝑚𝑚𝑖𝑛𝑖𝑚𝑢 where 0 ≤ yi ≥ 1,and (xi) is the value of any raster cell. where 0  yi  1,and (xi) is the value of any raster cell. where 0 ≤ yi ≥ 1,and (xi) is the value of any raster cell. Figure 7. Normalisation process. Figure 7. Normalisation process. Figure 7. Normalisation process. 3.6. AHP and FL 3.6. AHP and FL 3.6. AHP and FL AHP is a technique used to evaluate a group of factors, criteria or activities that affect AHP is a technique used to evaluate a group of factors, criteria or activities that AHP is a technique used to evaluate a group of factors, criteria or activities that affect a specific phenomenon to varying degrees [80]. Although AHP was proposed in the 1980s, affect a specific phenomenon to varying degrees [80]. Although AHP was proposed in the a specific phenomenon to varying degrees [80]. Although AHP was proposed in the 1980s, it remains an essential analysis method for subjects involving many options when per- 1980s, it remains an essential analysis method for subjects involving many options when it remains an essential analysis method for subjects involving many options when per- forming a pairwise comparison of the options is difficult. FAHP is an enhanced version of performing a pairwise comparison of the options is difficult. FAHP is an enhanced version forming a pairwise comparison of the options is difficult. FAHP is an enhanced version of AHP that supports a methodical alternative choosing rationale [43]. The traditional AHP of AHP that supports a methodical alternative choosing rationale [43]. The traditional AHP AHP that supports a methodical alternative choosing rationale [43]. The traditional AHP method is accompanied by uncertainty because of crisp value judgements; thus, it does method is accompanied by uncertainty because of crisp value judgements; thus, it does method is accompanied by uncertainty because of crisp value judgements; thus, it does not reflect human reasoning. Accordingly, FAHP was ultimately used to address this is- not reflect human reasoning. Accordingly, FAHP was ultimately used to address this issue not reflect human reasoning. Accordingly, FAHP was ultimately used to address this is- sue and achieve a more confident decision. The two methods were selected and used in and achieve a more confident decision. The two methods were selected and used in the sue and achieve a more confident decision. The two methods were selected and used in the current research. The AHP method was first applied to organise the hierarchical form current research. The AHP method was first applied to organise the hierarchical form and the current research. The AHP method was first applied to organise the hierarchical form and calculate the consistency ratio (CR) when investigating the consistency degree be- calculate the consistency ratio (CR) when investigating the consistency degree between and calculate the consistency ratio (CR) when investigating the consistency degree be- tween the weights of different values. Subsequently, the FAHP technique was used to the weights of different values. Subsequently, the FAHP technique was used to obtain the tween the weights of different values. Subsequently, the FAHP technique was used to Earth 2022, 3 712 criterion weights of the major domains (environment, urban, building and social) and the sub-criteria according to the following sequential steps: 1. Creating a pairwise comparison matrix. A pairwise comparison matrix was prepared based on the questionnaire survey results. Nine experts compared the relevant criteria with vulnerability indicators. The related weights of these criteria based on AHP were computed. To examine the consistency grade between the weighted values of various parameters, CR was calculated using the three formals (5)–(7). The results showed that the CR values were less than 0.1; thus, the pairwise comparison matrices were suitable. a  w 1 ij i = , (5) max n w i=1 where (a ) is a pairwise comparison matrix element, and (w ) is the weight value of ij i each parameter. ( n) max CI = (6) n 1 ( ) CI CR = (7) RI CI denotes the consistency index, whilst RI represents the mean of the random index that was calculated in accordance with Saaty’s rating RI (1–10) [81]. 2. The comparative importance hierarchy values are crisp in AHP. Thus, crisp values were transformed into fuzzy numbers in this step based on the triangular fuzzy mem- bership equation, i.e., Formula (8). Fuzzy value is described by three determinations {a, b, c}, as illustrated in Figure 8. 0, x < a > x a a  x  b b a m = (8) traingle(x) c x > b  x  c c b 0, x > c 3. In this step, the fuzzy geometric mean value (er) of every criterion was calculated using Formula (9). n o 1/n n n e e e e e e er = (A ) = A  A  A  A . . . A (9) i Õ ij i1 i2 i3 i4 in j=1 4. The fourth step was the determination of the fuzzy comparative weight of each criterion, as follows: we = er  (er +er +er +er + . . . er ) (10) i i 1 2 3 4 where (A ) is a fuzzy comparison matrix of dimension i to criterion j. ij 5. Determining the weights of the crisp values using the centre of area (COA) method based on Formula (11). (Lwe + M we + U we ) i i i w = (11) 6. The final step was the standardisation of the relative weights (w ) by applying Formula (12), and lastly, collecting the final weight (W ). Table 6 provides the results. ni W = , w = 1, w > 0 (12) ni å i i i=1 Earth 2022, 3, 14 obtain the criterion weights of the major domains (environment, urban, building and so- cial) and the sub-criteria according to the following sequential steps: 1. Creating a pairwise comparison matrix. A pairwise comparison matrix was prepared based on the questionnaire survey results. Nine experts compared the relevant crite- ria with vulnerability indicators. The related weights of these criteria based on AHP were computed. To examine the consistency grade between the weighted values of Earth 2022, 3 713 various parameters, CR was calculated using the three formals (5, 6 and 7). The re- sults showed that the CR values were less than 0.1; thus, the pairwise comparison matrices were suitable. Table 6. Results of the FAHP method. 1 𝑎 × 𝑤 λ = , (5) Partial Weight = Sub-Weight Major Criteria Major Weight Sub-Criteria Sub-W 𝑛 eight 𝑤 Major Weight Class A (c1) 0.221 0.097 where (aij) is a pairwise comparison matrix element, and (wi) is the weight value of each parameter. Class B (c2) 0.128 0.057 Environment domain (A) (λ −𝑛 ) 0.441 Class C (c3) 0.076 0.034 𝐶𝐼 = (6) ( ) 𝑛− 1 Weapon effects (c4) 0.575 0.254 𝐶𝐼 Subtotal 1.000 0.441 (7) 𝐶𝑅 = Informal settlements (c5) 0.644 0.175 CI denotes the consistency index, whilst RI represents the mean of the random index Building domain (B) Lack of infrastructure (c6) 0.356 0.097 that was calculated in accordance with Saaty’s rating RI (1–10) [81]. Subtotal 1.000 0.271 2. The comparative importance hierarchy values are crisp in AHP. Thus, crisp values Population density (c7) 0.491 0.071 were transformed into fuzzy numbers in this step based on the triangular fuzzy Housing density (c8) 0.255 0.037 membership equation, i.e., Formula (8). Fuzzy value is described by three determi- Urban domain (C) 0.144 Green space (c9) 0.255 0.037 nations {a, b, c}, as illustrated in Figure 8. Subtotal 1.000 0.144 0, 𝑥 < 𝑎 Health services (c10) 0.255 0.037 𝑥−𝑎 𝑎 𝑥 𝑏 Educational services (c11) 0.255 0.037 Social domain (D) 0.144 𝑏−𝑎 (8) Unemployment µ rate (c12) = 0.491 0.071 () 𝑐− 𝑥 𝑏 𝑥 𝑐 Subtotal 1.000 0.144 𝑐−𝑏 Total = 1 = 1 1.000 å å 0, 𝑥 > 𝑐 Figure 8. Triangular fuzzy membership equation. Figure 8. Triangular fuzzy membership equation. 3.7. WLC 3. In this step, the fuzzy geometric mean value (r˜) of every criterion was calculated Using WLC, the acquired weights from FAHP were entered on the basis of Formula (13) using Formula (9). to aggregate each group of sub-criteria into a single layer [82,83]. The results were four individual vulnerability maps: environmental vulnerability (V ), building vulnerability (V ), social vulnerability (V ) and urban vulnerability (V ), as shown in Figure 4. s u V = c w (q ) (13) e,b,u,s å i i i i=0 where n is the number of sub-criteria, (w ) is the relative weight of a sub-criterion (c ) and i i (q ) is the amount of a sub-criterion (c ). i i 𝑅𝐼 Earth 2022, 3, 16 where n is the number of sub-criteria, (wi ) is the relative weight of a sub-criterion (ci) and Earth 2022, 3 714 (qi) is the amount of a sub-criterion (ci). 3.8. Final Fuzzy Map 3.8. Final Fuzzy Map 3.8.1. Aggregated Vulnerability (Va) 3.8.1. Aggregated Vulnerability (Va) In accordance with the theory of vulnerability, the aggregated vulnerability (Va) was In accordance with the theory of vulnerability, the aggregated vulnerability (Va) was taken to be the product of the three vulnerability domains: urban, social and building, Va taken to be the product of the three vulnerability domains: urban, social and building, = Vu. Vs.Vb [31]. FL was used to consider uncertainty in the classification and combination Va = Vu  Vs  Vb [31]. FL was used to consider uncertainty in the classification and com- of the vulnerability indicators. Similarly, the FO tool was used to examine the potential of bination of the vulnerability indicators. Similarly, the FO tool was used to examine the an event relevant to various sets in a multi-criterion overlay examination. Although FO potential of an event relevant to various sets in a multi-criterion overlay examination. specifies which sets a phenomenon is possibly a member of, it also examines the relation- Although FO specifies which sets a phenomenon is possibly a member of, it also examines ships amongst members of various sets [49]. The FO tool was applied twice (fuzzy product the relationships amongst members of various sets [49]. The FO tool was applied twice and fuzzy sum) to obtain the best indicator of vulnerability by calculating FOG using For- (fuzzy product and fuzzy sum) to obtain the best indicator of vulnerability by calculating mula (14), as shown in Figure 9. FOG using Formula (14), as shown in Figure 9. ( ) ( ) 1g g n n 𝑉 = 𝑓 . 1 − (1 − 𝑓 ) (14) , () ( ) V = f . 1 1 f (14) a,t Õ (c) Õ (c) ; ; c;1 c;1 where n is the number of input rasters, f is the value of the pixel of each input raster, where n is the number of input rasters, f(c) is the value of the pixel of each input raster, γ (c) g is gamma (0.90) and V is the fuzzy gamma map of aggregation vulnerability and is gamma (0.90) and Va,t is the fuzzy gamma map of aggregation vulnerability and com- a ,t comprehensive vulnerability. prehensive vulnerability. Figure 9. Figure 9. FOG FOG was use was used d to to aggregate the thr aggregate the three vulnerabi ee vulnerability lity indi indicators cators (u (urban, rban, socia social l and and bu building) ilding) into a single indicator, called the aggregated vulnerability Indicator. into a single indicator, called the aggregated vulnerability Indicator. 3.8.2. Comprehensive Vulnerability Maps (Vt) 3.8.2. Comprehensive Vulnerability Maps (Vt) The final fuzzy map presents a comprehensive vulnerability map of the study area The final fuzzy map presents a comprehensive vulnerability map of the study area that was produced by multiplying the environment vulnerability indicators (Ve) by the that was produced by multiplying the environment vulnerability indicators (Ve) by the aggregated vulnerability indicators (Va) based on vulnerability theory, in accordance with aggregated vulnerability indicators (Va) based on vulnerability theory, in accordance with Formula 15 using FOG, as shown in Figure 10. To reduce evaluation subjectivity, two Formula 15 using FOG, as shown in Figure 10. To reduce evaluation subjectivity, two dif- different scenarios of the overall vulnerability maps were proposed. In the first scenario, ferent scenarios of the overall vulnerability maps were proposed. In the first scenario, each each of the four domains (environment, urban, social and building) was weighted with the of the four domains (environment, urban, social and building) was weighted with the ob- obtained value from the FAHP method. In the second scenario, each of the four domains tained value from the FAHP method. In the second scenario, each of the four domains had had the same value. the same value. Vt = Vu Vs Vb Ve (15) (15) Vt = Vu ∗ Vs ∗ Vb ∗ Ve Earth 2022, 3 715 Earth 2022, 3, 17 Figure 10. FOG was used to aggregate the four vulnerability indicators (environment, urban, social Figure 10. FOG was used to aggregate the four vulnerability indicators (environment, urban, social and and bu building) ilding)into into a s a single ingle ind indicator icator, , called called the the com compr pehensive rehensive vu vulnerabil lnerabil ity ity indicator indicator . . 3.9. Jenks Optimisation Method 3.9. Jenks Optimisation Method To understand vulnerability maps produced in this study and to characterise the data To understand vulnerability maps produced in this study and to characterise the data visually, the Jenks natural breaks (JNB) classification technique was used to reclassify the visually, the Jenks natural breaks (JNB) classification technique was used to reclassify the numerical values of the spatial data. The JNB technique utilises an algorithm that aims to numerical values of the spatial data. The JNB technique utilises an algorithm that aims to minimise the deviation of weight in each type from the type average [84]. Furthermore, this minimise the deviation of weight in each type from the type average [84]. Furthermore, algorithm attempts to increase the deviation of weights from the average of the other types this algorithm attempts to increase the deviation of weights from the average of the other on the basis of Formula (16) [85,86]. Consequently, the vulnerability maps were reclassified types on the basis of Formula (16) [85,86]. Consequently, the vulnerability maps were re- into five classes (very high, high, medium, low and very low) to enable decision-makers to classified into five classes (very high, high, medium, low and very low) to enable decision- interpret the results easily. makers to interpret the results easily. (SDAM SDCM) GVF = (16) (SDAM − SDCM) SDAM (16) GVF = 𝑆𝐷𝐴𝑀 where GVF is between 1 and 0, and represents the goodness of fit of the different proper variables; SDAM represents the total of the squared deviations from the average of the where GVF is between 1 and 0, and represents the goodness of fit of the different proper current array; and SDCM represents the total of the squared deviations from the average of variables; SDAM represents the total of the squared deviations from the average of the each type. current array; and SDCM represents the total of the squared deviations from the average of each type. 4. Results The results were six vulnerability maps produced at the neighbourhood scale of the 4. Results study area (Nasiriyah City): 1—urban vulnerability map (Vu), 2—social vulnerability map The results were six vulnerability maps produced at the neighbourhood scale of the (Vs), 3—building vulnerability map (Vb), 4—aggregated vulnerability map (Va), comprising study area (Nasiriyah City): 1—urban vulnerability map (Vu), 2—social vulnerability map three domains (urban, social and building), 5—environmental vulnerability map (Ve), and (Vs), 3—building vulnerability map (Vb), 4—aggregated vulnerability map (Va), compris- 6—final fuzzy map (Vt), as well as overall vulnerability maps (urban, social, building ing three domains (urban, social and building), 5—environmental vulnerability map (Ve), and environment). To provide understandable vulnerability maps, JNB classification was and 6—final fuzzy map (Vt), as well as overall vulnerability maps (urban, social, building used to classify the study area into five classes, depending on the proposed vulnerability and environment). To provide understandable vulnerability maps, JNB classification was indicators, from a very high vulnerability region to a very low vulnerability region. used to classify the study area into five classes, depending on the proposed vulnerability indicators, from a very high vulnerability region to a very low vulnerability region. 4.1. Urban Vulnerability Map Based on Formula (13), an urban vulnerability map (Vu) was produced by the over- 4.1. Urban Vulnerability Map lapping of sub-criteria, namely, dwelling density (C7), population density (C8) and green Based on Formula (13), an urban vulnerability map (Vu) was produced by the over- space ratio (C9), as shown in Figure 11. In Figure 12, 13 neighbourhoods of the city are lapping of sub-criteria, namely, dwelling density (C7), population density (C8) and green shown to be located in the very high vulnerability region. More than 196,928 people, i.e., space ratio (C9), as shown in Figure 11. In Figure 12, 13 neighbourhoods of the city are 28% of the city’s total population, are located in this region. In addition, eight neighbour- shown to be located in the very high vulnerability region. More than 196,928 people, i.e., hoods with more than 89,000 people, or 12% of the total population, are located in the high 28% of the city’s total population, are located in this region. In addition, eight neighbour- vulnerability region. hoods with more than 89,000 people, or 12% of the total population, are located in the high vulnerability region. Earth 2022, 3, 18 Earth 2022, 3 716 Earth 2022, 3, 18 (a) (b) (c) (a) (b) (c) Figure 11. Sub-criteria of the urban domain: (a) dwelling density (C7), (b) population density (C8) Figure 11. Sub-criteria of the urban domain: (a) dwelling density (C7), (b) population density (C8) Figure 11. Sub-criteria of the urban domain: (a) dwelling density (C7), (b) population density (C8) and (c) green space ratio (C9). and (c) green space ratio (C9). and (c) green space ratio (C9). Figure 12. Urban vulnerability map (Vu). Figure 12. Urban vulnerability map (Vu). Figure 12. Urban vulnerability map (Vu). 4.2. Social Vulnerability Map Using the same technique, a social vulnerability map (Vs) was produced by over- lapping sub-criteria, namely, education services (C10), health care services (C11) and unemployment rate (C12), as shown in Figure 13. In the process of gathering sub-criteria, Earth 2022, 3, 19 4.2. Social Vulnerability Map Earth 2022, 3, 19 Using the same technique, a social vulnerability map (Vs) was produced by overlapping sub-criteria, namely, education services (C10), health care services (C11) and unemployment rate (C12), as shown in Figure 13. In the process of gathering sub-criteria, each cr 4.2 it.erion Social was mu Vulnerability ltiplie Map d by the relevant weight obtained from FAHP using Formula Earth 2022, 3 717 (14) to produce Vs, as shown in Figure 14. Using the same technique, a social vulnerability map (Vs) was produced by overlapping sub-criteria, namely, education services (C10), health care services (C11) and unemployment rate (C12), as shown in Figure 13. In the process of gathering sub-criteria, each criterion was multiplied by the relevant weight obtained from FAHP using Formula each criterion was multiplied by the relevant weight obtained from FAHP using Formula (14) to produce Vs, as shown in Figure 14. (14) to produce Vs, as shown in Figure 14. (a) (b) (c) Figure 13. Sub-criteria of the social domain: (a) education index (C10), (b) health care index (C11) and (c) unemployment rate (C12). As shown in Figure 14, 10 neighbourhoods of the city are located in the very high vulnerability (a) (b) (c) region. These neighbourhoods are home to more than 197,559 people, i.e., 28% of the total popula- Figure 13. Sub-criteria of the social domain: (a) education index (C10), (b) health care index (C11) tion. In addition, 18 neighbourhoods with more than 172,000 residents, i.e., about 24% of the total Figure 13. Sub-criteria of the social domain: (a) education index (C10), (b) health care index (C11) and (c) unemployment rate (C12). population, are located in the high vulnerability region. and (c) unemployment rate (C12). As shown in Figure 14, 10 neighbourhoods of the city are located in the very high vulnerability region. These neighbourhoods are home to more than 197,559 people, i.e., 28% of the total popula- tion. In addition, 18 neighbourhoods with more than 172,000 residents, i.e., about 24% of the total population, are located in the high vulnerability region. Figure 14. Social vulnerability map (Vs). Figure 14. Social vulnerability map (Vs). As shown in Figure 14, 10 neighbourhoods of the city are located in the very high vulnerability region. These neighbourhoods are home to more than 197,559 people, i.e., 28% of the total population. In addition, 18 neighbourhoods with more than 172,000 residents, Figure 14. Social vulnerability map (Vs). i.e., about 24% of the total population, are located in the high vulnerability region. 4.3. Building the Vulnerability Map The building vulnerability map (Vb) was created by overlapping two sub-criteria, i.e., informal settlement rate (C5) and the lack of infrastructure rate (C6), using the same technique mentioned earlier, as shown in Figures 15 and 16. The latter shows that three Earth 2022, 3, 20 Earth 2022, 3, 20 4.3. Building the Vulnerability Map 4.3. Building the Vulnerability Map The building vulnerability map (Vb) was created by overlapping two sub-criteria, The building vulnerability map (Vb) was created by overlapping two sub-criteria, i.e., informal settlement rate (C5) and the lack of infrastructure rate (C6), using the same i.e., informal settlement rate (C5) and the lack of infrastructure rate (C6), using the same technique mentioned earlier, as shown in Figures 15 and 16. The latter shows that three Earth 2022, 3 718 technique mentioned earlier, as shown in Figures 15 and 16. The latter shows that three neighbourhoods of the city are located in the very high vulnerability region. These neigh- neighbourhoods of the city are located in the very high vulnerability region. These neigh- bourhoods have more than 23,000 residents, i.e., 3% of the city’s total population. In addi- bourhoods have more than 23,000 residents, i.e., 3% of the city’s total population. In addi- tion, only seven neighbourhoods with more than 36,000 residents, i.e., 5% of the total pop- tion, only seven neighbourhoods with more than 36,000 residents, i.e., 5% of the total pop- neighbourhoods of the city are located in the very high vulnerability region. These neigh- ulation, are located in the high vulnerability region. ulation, are located in the high vulnerability region. bourhoods have more than 23,000 residents, i.e., 3% of the city’s total population. In addition, only seven neighbourhoods with more than 36,000 residents, i.e., 5% of the total population, are located in the high vulnerability region. (a) (b) (a) (b) Figure 15. Sub-criteria of the building domain: (a) informal settlement rate (C5) and (b) lack of infra- Figure 15. Sub-criteria of the building domain: (a) informal settlement rate (C5) and (b) lack of infra- Figure 15. Sub-criteria of the building domain: (a) informal settlement rate (C5) and (b) lack of structure rate (C6). structure rate (C6). infra-structure rate (C6). Figure 16. Building vulnerability map (Vb). Figure 16. Building vulnerability map (Vb). 4.4. Aggregated Vulnerability Map Figure 16. Building vulnerability map (Vb). The aggregated vulnerability map (Va) was produced by multiplying Vu, VS and Vb using the FOG technique based on Formula (14). As shown in Figure 17, six neighbour- hoods of the city are located in the very high vulnerability region. These neighbourhoods have more than 106,000 residents, or 15% of the total population. In addition, 17 neigh- bourhoods with more than 204,000 people, or 29% of the total population, live in the high vulnerability area. Table 7 provides the spatial distribution of the population based on the Earth 2022, 3, 21 4.4. Aggregated Vulnerability Map The aggregated vulnerability map (Va) was produced by multiplying Vu, VS and Vb using the FOG technique based on Formula (14). As shown in Figure 17, six neighbour- hoods of the city are located in the very high vulnerability region. These neighbourhoods have more than 106,000 residents, or 15% of the total population. In addition, 17 neigh- Earth 2022, 3 719 bourhoods with more than 204,000 people, or 29% of the total population, live in the high vulnerability area. Table 7 provides the spatial distribution of the population based on the proposed vulnerability indicators , while Table A1 provides the codes and names of the neighbourho proposed ods of Nasiriyah C vulnerability indicators, ity in south Iraq. while Table A1 provides the codes and names of the neighbourhoods of Nasiriyah City in south Iraq. Figure 17. Classification of city neighbourhoods based on the aggregated vulnerability indicators (Va). Table 7. Classification of city neighbourhoods based on the aggregated vulnerability indicators. Figure 17. Classification of city neighbourhoods based on the aggregated vulnerability indicators (Va). Vulnerability Number of Neighbourhood Ratio from the Population Area (Hectares) Indicators Neighbourhoods Code Total Population Table 7. Classification of city neighbourhoods based on the aggregated vulnerability indicators. 38, 53, 56, 57, 62 Very high 6 106,809 15% 267 and 79 Vulnerability Number of Ratio from the Total Neighbourhood Code Population Area (Hectares) High 17 204,762 29% 557 Indicators Neighbourhoods Population Very high 6 38, 53, 56, 57, 62 and 79 106,809 15% 267 Medium 14 119,185 17% 433 High 17 204,762 29% 557 Low 11 77,596 11% 329 Medium 14 119,185 17% 433 Very low 13 61,194 9% 366 Low 11 77,596 11% 329 Very low 13 61,194 9% 366 4.5. Environmental Vulnerability Map (Ve) Overlapping of sub-criteria, i.e., high sources of pollution (C1), moderate sources of pollution (C2), low sources of pollution (C3) and effects of weapons and wars, DU landfill (C4), produced the environmental vulnerability map based on Formula (14), as shown in Figures 18–20. The environmental vulnerability map classifies the study area into five classes, as mentioned earlier. For higher accuracy, the values of pixels were extracted from the boundaries of each neighbourhood based on its coordinates. Figure 22 shows that six neighbourhoods of the city are located in the very high vulnerability area. More than 68,660 people, i.e., 9.7% of the total population of the study area, are living in these neigh- bourhoods, which are exposed to environmental risks. In addition, eight neighbourhoods with more than 38,000 residents, or 5.4% of the total population, are located in the high vulnerability region. Earth 2022, 3, 22 4.5. Environmental Vulnerability Map (Ve) Overlapping of sub-criteria, i.e., high sources of pollution (C1), moderate sources of pollution (C2), low sources of pollution (C3) and effects of weapons and wars, DU landfill (C4), produced the environmental vulnerability map based on Formula (14), as shown in Figures 18–20. The environmental vulnerability map classifies the study area into five classes, as mentioned earlier. For higher accuracy, the values of pixels were extracted from the boundaries of each neighbourhood based on its coordinates. Figure 21 shows that six neighbourhoods of the city are located in the very high vulnerability area. More than 68,660 people, i.e., 9.7% of the total population of the study area, are living in these neigh- bourhoods, which are exposed to environmental risks. In addition, eight neighbourhoods with more than 38,000 residents, or 5.4% of the total population, are located in the high Earth 2022, 3 720 vulnerability region. (a) (b) Earth 2022, 3, 23 Figure 18. Sub-criteria of the environmental domain: (a) map of the high sources of pollution (C1) Figure 18. Sub-criteria of the environmental domain: (a) map of the high sources of pollution (C1) and (b) map of the moderate sources of pollution (C2). and (b) map of the moderate sources of pollution (C2). (a) (b) Figure 19. Sub-criteria of the environmental domain: (a) map of the low-polluting projects (C3) and (b) buffer zones of DU landfill (C4). Figure 19. Sub-criteria of the environmental domain: (a) map of the low-polluting projects (C3) and (b) buffer zones of DU landfill (C4). Earth 2022, 3, 24 Earth 2022, 3 721 Figure 20. Environmental vulnerability map (Ve). 4.6. Comprehensive Vulnerability Map Figure 20. Environmental vulnerability map (Ve). By using the FOG function based on Formula (14), a first scenario of the comprehen- sive vulnerability map (Vt) was produced by multiplying Va by Ve. As shown in Figure 22, 11 city neighbourhoods are located in the very high vulnerability region. They are home to more than 175,000 residents, or 25% of the total population of the study area. Furthermore, 12 neighbourhoods with more than 115,000 residents, or 16% of the total population, are located in the high vulnerability region, as indicated in Table 8. The second scenario results showed that only five neighbourhoods with 104,000 residents, i.e., 15% of the total popula- tion, are located in the very high vulnerability region. Furthermore, 15 neighbourhoods Earth 2022, 3 722 with more than 202,208 persons, or 29% of the total population, are located in the high vulnerability region, as shown in Figure 21. Table 9 provides the second scenario results. Table 8. First classification scenario of city neighbourhoods based on the comprehensive vulnerability index (Vt). Vulnerability Number of Neighbourhood Ratio from the Population Area (Hectares) Indicators Neighbourhoods Code Total Population 6, 38, 53, 54, 55, 56, Very high 11 175,678 25% 431 57, 61, 76, 79, 85 High 12 115,841 16% 336 Medium 14 145,345 21% 503 Earth 2022, 3, 27 Low 13 93,033 13% 388 Very low 11 39,649 6% 293 Figure 22. Figure Second clas 21. Second sificati classification on scenario of scenario city neig of city hbou neighbour rhoods ba hoods sed on based the com on the p compr rehensive ehensive vul-vulner- nerability index (Vt). ability index (Vt). 4.7. Validation 4.7.1. Using Machine Learning (ML) The ML technique was used to verify the accuracy and robustness of the vulnerability map classification. ML is a technique that uses a small part of the data (the testing dataset) to evaluate a large part of the same dataset (a trained sample) [87]. The naïve Bayes (NB) classifier that is available in the Weka software was applied on the basis of Formula (17) [88]. Weka software is a group of ML algorithms for mining data; it is an open-source application [89]. The validation result showed that correctly classified instances were 90.4762%, and the kappa statistic value was 0.8786. Thus, the level of agreement of the classification results was demonstrated to be ‘almost perfect’. If the kappa value is be- tween 0.80 and 1, then the result is interpreted as an ‘almost perfect agreement’ [90]. ( ) 𝑃 𝑃 𝐴 (18) 𝑃 = 𝐵 𝑃(𝐵) P(A/B) presents the posterior likelihood, where (A) is the probability of the hypothe- sis, and (B) presents the observed event. P(B/A) represents probability: the probability of the proof given that the probability of a hypothesis is correct. Earth 2022, 3 723 Table 9. Second classification scenario of city neighborhoods based on the comprehensive vulnerabil- ity index (Vt). Vulnerability Number of Neighbourhood Ratio from the Population Area (Hectares) Indictors Neighbourhoods Code Total Population Very high 5 38, 53, 54, 56, 61 104,844 15% 255 High 15 202,208 29% 509 Medium 15 120,049 17% 484 Earth 2022, 3, Low 30 107,780 15% 430 26 Very low 11 34,665 5% 302 Figure 21. Figure First cla 22. ssif First icat classification ion scenario of scenario city neig of city hbourhoo neighbour ds ba hoods sed on th based e comprehensi on the compr vehensive e vulner- vulnera- ability index (Vt). bility index (Vt). Table 9. Second classification scenario of city neighborhoods based on the comprehensive vulnera- 4.7. Validation bility index (Vt). 4.7.1. Using Machine Learning (ML) The ML technique was used to verify the accuracy and robustness of the vulnerability Vulnerability Number of Ratio from the Total Neighbourhood Code Population Area (Hectares) map classification. ML is a technique that uses a small part of the data (the testing dataset) Indictors Neighbourhoods Population to evaluate a large part of the same dataset (a trained sample) [87]. The naïve Bayes (NB) Very high 5 38, 53, 54, 56, 61 104,844 15% 255 classifier that is available in the Weka software was applied on the basis of Formula (17) [88]. High 15 202,208 29% 509 Medium 15 120,049 17% 484 Low 30 107,780 15% 430 Very low 11 34,665 5% 302 Earth 2022, 3 724 Weka software is a group of ML algorithms for mining data; it is an open-source applica- tion [89]. The validation result showed that correctly classified instances were 90.4762%, and the kappa statistic value was 0.8786. Thus, the level of agreement of the classification results was demonstrated to be ‘almost perfect’. If the kappa value is between 0.80 and 1, then the result is interpreted as an ‘almost perfect agreement’ [90]. P P(A) A A P = (17) B P(B) P(A/B) presents the posterior likelihood, where (A) is the probability of the hypothesis, Earth 2022, 3, 28 and (B) presents the observed event. P(B/A) represents probability: the probability of the proof given that the probability of a hypothesis is correct. 4.7.2. Spatial Analysis Validation 4.7.2. Spatial Analysis Validation Three aerial photos of the study area were acquired, i.e., drone imagery (2009), Plei- Three aerial photos of the study area were acquired, i.e., drone imagery (2009), ades 1 ORTHO (2014) and Sentinel 2 imagery (October 2021), to validate the results of the Pleiades 1 ORTHO (2014) and Sentinel 2 imagery (October 2021), to validate the results comprehensive vulnerability map (Vt), as shown in Figure 21. Six neighbourhoods from of the comprehensive vulnerability map (Vt), as shown in Figure 22. Six neighbourhoods the eleven located in the very high vulnerability region (61, 54, 55, 56, 53 and 57) were from the eleven located in the very high vulnerability region (61, 54, 55, 56, 53 and 57) were spatially analysed as a sample to validate the vulnerability indicator results. Figure 23 spatially analysed as a sample to validate the vulnerability indicator results. Figure 23 shows the neighbourhoods located in the buffer zone of the main WWTP of the city, a shows the neighbourhoods located in the buffer zone of the main WWTP of the city, a high pollution source. In addition, the streets of this area are dusty (unpaved streets), and high pollution source. In addition, the streets of this area are dusty (unpaved streets), more than half of the total dwellings are informal housing. Compared with the other and more than half of the total dwellings are informal housing. Compared with the other neighbourhoods, these are the most vulnerable areas. The spatial analysis confirmed the neighbourhoods, these are the most vulnerable areas. The spatial analysis confirmed the validity of the results. validity of the results. Figure 23. Spatial analysis process for the region with very high vulnerability. Figure 23. Spatial analysis process for the region with very high vulnerability. Earth 2022, 3, 29 Earth 2022, 3 725 4.7.3. Sensitivity Analysis 4.7.3. Sensitivity Analysis Sensitivity analysis was used to investigate the model sensitivity to different criterion Sensitivity analysis was used to investigate the model sensitivity to different criterion weights. It is typically applied as a mechanism for assessing the responses of a model to weights. It is typically applied as a mechanism for assessing the responses of a model to modifying the input parameters and evaluating the reliability of the obtained results [91]. modifying the input parameters and evaluating the reliability of the obtained results [91]. Thus, model outcomes are substantial if the study results are altered when the input Thus, model outcomes are substantial if the study results are altered when the input weights of the criteria are different [92–94]. In this study, a sensitivity analysis process weights of the criteria are different [92–94]. In this study, a sensitivity analysis process was was applied to demonstrate the effect of different weights on the classification outcomes applied to demonstrate the effect of different weights on the classification outcomes to to verify the robustness or sensitivity of the proposed model versus the relative im- verify the robustness or sensitivity of the proposed model versus the relative importance portance of the major criteria. In addition, sensitivity analysis addresses the hypothesis of the major criteria. In addition, sensitivity analysis addresses the hypothesis that the that the study results will be changed if another scenario is used. In this context, another study results will be changed if another scenario is used. In this context, another scenario scenario was prepared in which the weights of the major criteria were changed. The clas- was prepared in which the weights of the major criteria were changed. The classes of the ses of the city’s neighbourhoods were changed when the new weighted values were in- city’s neighbourhoods were changed when the new weighted values were inputted, as putted, as illustrated in Figure 24. Consequently, the sensitivity analysis process con- illustrated in Figure 24. Consequently, the sensitivity analysis process confirmed that the firmed that the model results were robust. model results were robust. (a) (b) Figure 24. Change in a neighbourhood’s classification between the first scenario (a) and the second Figure 24. Change in a neighbourhood’s classification between the first scenario (a) and the second scenario (b). scenario (b). 5. Discussion Previous studies have used different methods to define vulnerable urban areas, and have adopted various criteria that are relevant to vulnerability assessment. Some scholars, such as Hazell (2020), classified the criteria into three categories (topographic, land cover attribute and demographic), whilst others, such as Gerundo, Marra and de Salvatore (2020), classified criteria into social, urban and building domains [7,28]. Similarly, Ruá et al. (2021) classified criteria into four categories: socioeconomic, sociodemographic, urban and building [30]. However, most of these approaches have disregarded potential environmental issues in urban areas resulting from urbanisation and human activities. Instead, they have focused primarily on financial and social criteria for studying land use Earth 2022, 3 726 change. Furthermore, the simulation results obtained were difficult to utilise in optimising land use on Earth [33]. In contrast with conventional approaches, the current study involved a new approach that is capable of comprehensively measuring vulnerability indicators (Vt), including environmental vulnerability indicators. Compared with previous studies, in which classical theory based on the logic of crisp sets was used, in the current study, FL was used to consider the uncertainty in the classification and combination of vulnerability indicators. In addition, the FOG function was applied to produce the final fuzzy map to balance the rising effect of the fuzzy sum and the lessening effect of the fuzzy product to obtain the best result. In contrast to previous studies that used various criteria derived only from the existing literature, the current study involved a conservative approach to confirm the relevance of the criteria with the actual reality of vulnerable urban areas. The proposed approach included three sequential phases, starting with selecting relevant criteria from the literature review, then using the Delphi technique to arrive at the group’s opinion to endorse these criteria, and relating the endorsed criteria to national urban and environmental indicators. Furthermore, this study used the JNB method to provide a more meaningful visualisation for the vulnerability maps. ML was used to validate the model results. Two different scenarios of the overall vulnerability maps were created to reduce evaluation subjectivity. The results indicated, both visually and statistically, that the city neighbourhoods suffered from environmental pollution and regional marginalisation. A large area of the city was suffering from pollution effects, with residential land use overlapping with polluted industrial use because of rapid urbanisation and poor land use. In addition, the comprehensive vulnerability maps showed that many neighbourhoods were located in very high and high vulnerability regions. The western part of the city, which is involved in future city expansion based on the master plan approved by local authorities, is located in a region with environmental pollution. By contrast, the northern part of the study area is outside the region with environmental pollution, and thus is suitable for future city extension. The conclusion can be drawn that local urban planning standards and environmental legislation have been disregarded in the planning stages for urban development. With respect to the comparability of Nasiriyah City, i.e., the case study, and other Arab cities, the study produced results that were consistent, to a certain extent, with those of other studies that have been conducted to define vulnerable urban areas in Egyptian cities. For example, a study conducted by Effat, Ramadan and Ramadan (2021) in Assiut City, Egypt, revealed that the informal settlement rate, population density, urban growth rate and the lack of essential services are the most significant factors that increase the degree of vulnerability in urban areas [95]. Similarly, another study conducted by Waly, Ayad and Saadallah (2021) in Alexandria City, Egypt indicated that demographic charac- teristics, infrastructure indicators, urban domain, unemployment and poverty were the most consequential factors leading to urban vulnerability in the city [96]. However, the current study utilized a new approach that can evaluate urban areas more realistically by adopting comprehensive vulnerability indicators, including environmental indicators that are integrated with social, urban and building indicators. The proposed comprehensive assessment approach can be more reliable as a decision support system for analysing urban areas, and for allocating required financial resources and efficiently executing mitigation processes for the most vulnerable Arabic urban areas and developing countries. In summary, compared with previous techniques, the proposed approach, based on vulnerability theory, contributes to identifying priority areas of intervention, exhibits novelty and makes a significant contribution to Earth’s sustainability. The proposed integration, i.e., using aggregated vulnerability indicators coupled with environment vulnerability indicators, enables the building of a robust database and provides a guide for comprehensive vulnerability assessment, offering an improved decision support system to determine priority areas for intervention in complex urban areas. In addition, this system Earth 2022, 3 727 can help optimise public spending to mitigate vulnerability as local authorities responsible for city services frequently have insufficient financial resources. Why is the identification of vulnerable urban areas necessary before starting with intervention procedures? Evaluating a city’s situation before implementing intervention procedures has many purposes: 1—to identify the magnitude of the problem and clarify why a comprehensive plan for mitigating city problems with four dimensions (urban, social, building and environment) is urgently needed; 2—to define intervention priorities based on accurate vulnerability indicators; and, 3—to prepare a spatial database for monitoring vulnerability indicators when implementing intervention plans to mitigate the adverse effects of urbanisation and human activities. 6. Conclusions Although SEA was introduced in the 1990s as an effective mechanism for assessing the environmental effect of polluting activities to preserve Earth’s sustainability, most previous studies have overlooked environmental pollution when defining vulnerable ur- ban areas. The current research attempts to bridge the research gap by presenting a new comprehensive assessment model for defining vulnerable urban areas based on vulnerabil- ity theory with four dimensions: urban, social, building and environment. To overcome uncertainty in expert opinions and uncertainty in the classification and combination of vulnerability indicators, three FL techniques were integrated, FAHP, FLM and FOG, to ensure a comprehensive assessment of vulnerability. The proposed approach adopted twelve criteria organised into four domains (building, social, urban and environment) to define a vulnerable urban area. Furthermore, the proposed vulnerability indicators were classified using the JNB classification technique, and then the results were validated via ML. The validation model that used ML confirmed that the level of agreement of the classification results was ‘almost perfect’. Therefore, the model can facilitate making a wise decision, particularly when city districts are suffering simultaneously from diverse adverse effects. The major contribution of this study is the provision of a powerful decision support system for the assessment and analysis of urban areas that are exposed to environmental degradation and spatial marginalisation. This system can be used to allocate the required financial resources and ensure mitigation processes are executed efficiently for the most vulnerable urban areas in Iraq and other developing countries. However, strict restrictions are imposed on accessing data regarding environmental pollution and social vulnerability at the household scale to analyse the effects of polluting projects on human health in very high vulnerability regions. In addition, no actual investigations have determined the effect of DU in the study area due to a lack of experience and tools. Nevertheless, international reports have indicated an increase in disease cases associated with DU in the study area. Thus, further studies that focus on the effects of DU in conflict areas in general, and in Iraq in particular, are urgently needed. Improving the ability to evaluate overall vulnerability in urban areas under rapid urbanisation and high population growth will be essential when formulating policies for urban communities and building sustainable livelihoods in developing countries. Author Contributions: Conceptualization, S.K.H. and A.F.A.; methodology, S.K.H.; software, S.K.H.; validation, S.K.H., A.F.A., H.Z.M.S. and A.W.; formal analysis, S.K.H.; investigation, S.K.H. and A.F.A.; resources, S.K.H.; data curation, S.K.H.; writing—original draft preparation, S.K.H.; writing—review and editing, S.K.H. and A.F.A.; supervision, S.K.H., A.F.A., H.Z.M.S. and A.W. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: All data are reported in this paper. Conflicts of Interest: The authors declare no conflict of interest. Earth 2022, 3 728 Appendix A Table A1. The codes and names of the neighbourhoods of Nasiriyah City in south Iraq. Neighbourhood Neighbourhood Name Name Code Code 1 Aljamaa 46 Alaskary_3 2 Sawage 47 Alhasan 3 Alseray 48 Bashaeer 4 Syaf 49 Rasool_1 5 Sabeah 50 Rasool_2-3 6 Alsharqyah_1 53 Feda_2 7 Alsharqyah_2 54 Alamen dakhaly_1 8 AbuJada_1 55 Alamen dakhaly_2 9 AbuJada_2 56 Alamen dakhaly_3 10 Alarooba 57 Karama_1 11 AladaraAlmahalyah 58 Karama_2 12 Alsalhyah_1 59 Tadahayh_1 13 Alsalhyah_2 60 Tadahayh_2 14 Alsalhyah_3 61 Tadahayh_3 15 Shuhada_1 62 Zahra 16 Shuhada_2 63 Beqaa 17 Shuhada_3 64 Khadrah 18 Shuhada_4 65 old askan_1 19 Shuhada_5 67 Old askan_3 20 Rafedeen 68 old askan_4 21 Arido_1 69 Mutanazah 22 Arido_2 70 Zauyah_Bs 23 Arido_3 71 Alaarja 24 Arido_4 72 Mansuryah_1 25 Arido_5 73 Mansuryah_2 26 Ind_n_1 74 Mansuryah_3 27 Ind_n_2 75 Thura_1 28 Sader_1 76 Thura_2 29 Sader_2 77 Thura_3 30 Sader_3 78 Zaaylat 31 Sader_4 79 Zaaylat_2 32 Ur_1 80 Zaaylat_3 33 Ur_2 81 Samood_fayth 34 Ur_3 82 Samood_2 35 Ur_4 84 Shaalah 36 Sumer_1 85 Sakak 37 Sumer_2 86 Alaskan_Sanay 38 Sumer_3 87 Alhbush 39 Sumer_4 88 Alamarat 40 Almulmeen_1 89 Shmukh 41 Almulmeen_2 90 Kanzawy 42 Almulmeen_3 91 Sader ccomplex 43 Almulmeen_4 92 University complex 44 Alaskary_1 144 Khatra-2 45 Alaskary_2 References 1. 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Comprehensive Vulnerability Assessment of Urban Areas Using an Integration of Fuzzy Logic Functions: Case Study of Nasiriyah City in South Iraq

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Article Comprehensive Vulnerability Assessment of Urban Areas Using an Integration of Fuzzy Logic Functions: Case Study of Nasiriyah City in South Iraq 1 , 2 , 3 1 2 Sadeq Khaleefah Hanoon * , Ahmad Fikri Abdullah , Helmi Z. M. Shafri and Aimrun Wayayok Civil Engineering Department, Faculty of Engineering, University Putra Malaysia, Serdang 43400, Malaysia; helmi@upm.edu.my Biological and Agricultural Engineering Department, Faculty of Engineering, University Putra Malaysia, Serdang 43400, Malaysia; ahmadfikri@upm.edu.my (A.F.A.); aimrun@upm.edu.my (A.W.) International Institute of Aquaculture and Aquatic Sciences (I-AQUAS), University Putra Malaysia, Port Dickson 70150, Malaysia * Correspondence: gs58154@student.upm.edu.my Abstract: Globally, urbanisation has been the most significant factor causing land use and land cover changes due to accelerated population growth and limited governmental regulation. Urban communities worldwide, particularly in Iraq, are on the frontline for dealing with threats associated with environmental degradation, climate change and social inequality. However, with respect to the effects of urbanization, most previous studies have overlooked ecological problems, and have disregarded strategic environmental assessment, which is an effective tool for ensuring sustainable development. This study aims to provide a comprehensive vulnerability assessment model for urban areas experiencing environmental degradation, rapid urbanisation and high population growth, to Citation: Hanoon, S.K.; Abdullah, help formulate policies for urban communities and to support sustainable livelihoods in Iraq and A.F.; Shafri, H.Z.M.; Wayayok, A. other developing countries. The proposed model was developed by integrating three functions Comprehensive Vulnerability of fuzzy logic: the fuzzy analytic hierarchy process, fuzzy linear membership and fuzzy overlay Assessment of Urban Areas Using an gamma. Application of the model showed that 11 neighbourhoods in the study area, and more than Integration of Fuzzy Logic Functions: 175,000 individuals, or 25% of the total population, were located in very high vulnerability regions. Case Study of Nasiriyah City in The proposed model offers a decision support system for allocating required financial resources and South Iraq. Earth 2022, 3, 699–732. efficiently implementing mitigation processes for the most vulnerable urban areas. https://doi.org/10.3390/ earth3020040 Keywords: vulnerability; urban; environment; infrastructure; uranium; MCDM; fuzzy; GIS Academic Editor: George D. Bathrellos Received: 14 May 2022 Accepted: 5 June 2022 1. Introduction Published: 8 June 2022 Globally, rapid urbanisation to meet the needs of uncontrolled population growth has led to several challenges, such as pollution, congested traffic, poor sustainability and Publisher’s Note: MDPI stays neutral negative impacts on the natural environment [1,2]. Cities have expanded at the expense with regard to jurisdictional claims in published maps and institutional affil- of green areas, leading to environmental degradation [3,4]. Rapid expansion has resulted iations. in the proliferation of many human activities that are difficult to manage; consequently, significant impacts on ecology and public health are likely to arise [5,6]. In the context of uncontrolled urban sprawl, a lack of financial resources and expertise, coupled with spatial marginalisation, has exposed entire urban areas to degradation risks [7]. Communities in Copyright: © 2022 by the authors. vulnerable areas face significant challenges, such as access to suitable public buildings, and Licensee MDPI, Basel, Switzerland. the availability of electricity, transportation, government education, healthcare and water This article is an open access article supply [8]. To respond to these challenges, current techniques need to be enhanced to cope distributed under the terms and with the complex changes occurring to the urban environment [9,10]. conditions of the Creative Commons Locally, given that Iraq is facing the consequences of long wars (1980 to 2003), military Attribution (CC BY) license (https:// action has strongly affected land use and land cover changes. The wars experienced creativecommons.org/licenses/by/ have contributed to environmental degradation, including through the transformation of 4.0/). Earth 2022, 3, 699–732. https://doi.org/10.3390/earth3020040 https://www.mdpi.com/journal/earth Earth 2022, 3 700 rivers, scorched earth exercises, the annihilation of animals and plants, oil spills, burning of petroleum wells and the use of chemical and biological weapons [11,12]. Moreover, non-traditional weapons used in Gulf Wars I and II have exposed Iraq’s environment to the harmful effects of the use of radioactive weapons [13]. Although a high level of environmental degradation and significant changes in Iraq’s environment have occurred, suitable measures to protect the environment are still lacking [14]. Urban communities have been on the frontline in dealing with the challenges of ecological degradation, urbanisation and the occurrence of different pandemics [15,16]. The sustainable development goals (SDGs) are goals for achieving long-term sus- tainability on Earth. With respect to these, in the short term, improvement in techniques that can provide sustainable solutions for urban areas that are high vulnerable should be a primary objective [17]. Mitigation and enhancement processes in urban areas must integrate approaches that match the SDGs and be applied to the most vulnerable areas as a priority [18–20]. Vulnerable areas should be prioritized when launching urban interven- tions, whilst urban sprawl should be simultaneously monitored and controlled [21]. There are a number of different approaches to the design of indicators that can comprehensively define, evaluate and address vulnerability, by integrating traditional data sources with modern Earth observation data [22–25]. Some researchers have proposed deprivation indices to measure deprivation in urban areas, such as the English indices of deprivation (IoD 2019) [26]. Others, such as Lynch and Mosbah (2017), have developed local indices to comprehensively measure sustainability [27]. Studies that have applied vulnerability theory to identify vulnerable urban areas have tended to be more comprehensive because they have sought to include a wide range of factors that can affect urban environments. Vulnerability theory has been applied by many researchers in the urban planning field. Hazell (2020) proposed ten criteria, divided into three major categories, namely, topographic, demographic and land cover attributes, to identify potentially vulnerable populations and to characterise desirable urban environment quality [28]. Ge et al. (2019) presented sixteen primary indicators for assessing social vulnerability, divided into four major categories: health inequality, cultural inequality, economic inequality and social inequality [29]. Ruá et al. (2021) defined four major domains, including the urban do- main (UD), building domain (BD), sociodemographic domain (SD) and the socioeconomic domain (SE), to evaluate vulnerable urban areas [30]. Similarly, Gerundo, Marra and de Salvatore (2020) utilized three dimensions (i.e., social domain, UD and BD) to construct a composite vulnerability index for describing vulnerable urban areas [7]. In another study, conducted by Gerundo et al. (2020), the authors proposed a set of mitigation indicators for three major dimensions, the social domain, BD and UD, as useful tools for assessing vulnerability [31]. However, most models used in this context overlook ecological problems that are associated with urbanisation and disregard strategic environmental assessment (SEA), which is an effective technique for assessing environmental damage due to human activity to ensure that urban development is sustainable [32–34]. Therefore, the current study seeks to bridge this gap by presenting a comprehensive vulnerability assessment technique that can effectively define vulnerable urban areas and monitor urban sprawl. The approach is relevant to the environmental impact assessment of polluting activities in urban areas as a significant part of a total vulnerability evaluation. Many techniques are available for evaluating vulnerability in urban areas, including multi-criteria decision analysis (MCDA) for assessing multiple factors that contribute to the complexity of the urban fabric [35,36]. The integration of MCDA into a geographic information system (GIS) is commonly used to resolve various complicated spatial prob- lems. Furthermore, available remote-sensing (RS) datasets and expert opinion make such integration more efficient for supporting the decision-making process [37,38]. The approach enables the combining of data derived from different geographical factors into a single measurement index [39], to assess the reality of the situation and identify implications for ecological sustainability [19,35,40]. Although more than 15 different MCDA methods are currently available, the most notable is the analytic hierarchy process (AHP) [41,42]. How- Earth 2022, 3 701 ever, AHP applies crisp values, and its results are accompanied by uncertainty; thus, fuzzy AHP (FAHP) has emerged as an upgraded version of AHP that reflects human reasoning processes [43]. Indicators with multiple levels and weighted importance that result from FAHP can be compared to support decision-makers in defining optimal alternatives and indicators [2]. Whilst the AHP technique provides satisfactory results, FAHP deals with uncertainty values that are associated with vulnerability indicators [44]. Fuzzy logic (FL) is the most effective application of spatial analysis in the urban planning field which has been extensively improved as a significant function of GIS [45,46]. It can evaluate the different degrees of membership for complex topics associated with uncertainty, such as vulnerability indicators [7]. FL includes several types of functions. Fuzzy linear membership (FLM) is one of these functions; it can be operated with MCDA to standardise criteria to make wise decisions and convert various parameters into fuzzy values between 0 and 1 [47,48]. The fuzzy overlay (FO) function is applied when analysing the effects of various factors related to many sets in the multi-criteria overlay technique. The FO function analyses the relationships between the sub-criteria of multiple major criterion sets [49]. Furthermore, some significant functions are involved in FO that allow combining fuzzy membership values for diverse variables by performing a cell-by-cell overlay process [50,51]. FO gamma (FOG) is the most significant function that results from multiplying a fuzzy product value by a fuzzy sum value. Both values are raised to the power of gamma. FOG makes an adjustment between the increasing fuzzy sum value and the diminishing effect of the fuzzy product value [52,53]. The current study presents a new approach for the comprehensive vulnerability as- sessment of urban areas. The proposed approach takes advantage of effective fuzzy logic functions to overcome uncertainty in the classification and combination of vulnerability indicators, which represents a significant strategy for making sensitive decisions associated with human life. It was used to integrate (FAHP), (FLM) and (FOG) to derive a comprehen- sive vulnerability indicator for Nasiriyah City in Iraq. The comprehensive vulnerability indicator is an algebraic product of environmental vulnerability with urban vulnerability, building vulnerability and social vulnerability, produced in accordance with vulnerability theory to define vulnerable urban areas. The new approach enables building of a robust database and provision of relevant guidance for comprehensive vulnerability assessment, serving as an improved decision support system for determining priority intervention sites within complicated urban areas. In addition, the system enables optimisation of public spending for mitigating vulnerability given that local authorities responsible for city services frequently have insufficient financial resources. This approach can be applied to enhance policies formulated for urban communities and help build sustainable livelihoods in all regions of Iraq and other developing countries. 2. Vulnerability Indicators Vulnerability emerges from environmental, physical, economic, and social problems in urban areas. This term is used to describe a reduced capacity to adapt to, resist and recuperate from risks [54,55]. Thus, urban vulnerability can be described as a situation that arises from the combination of multiple disadvantageous factors leading to challenging circumstances that it is difficult for an urban community to overcome [56,57]. The recogni- tion and measurement of these factors is essential before implementing plans to mitigate vulnerability. The most suitable measurement approach is based on vulnerability theory; it combines different vulnerability indicators, such as social, urban, building and envi- ronmental indices, into a single indicator to represent the situation to support mitigation planning. This method enables diagnosis of urban problems and identification of solutions without requiring substantial data collection [58]. Collecting data associated with many indicators is extremely difficult; hence, the vulnerability assessment process can be accomplished by focusing on different indicators dependent on local conditions or data availability [59]. A comprehensive vulnerability assessment based on vulnerability theory was performed for the study area (Nasiriyah City, Earth 2022, 3 702 Iraq) to define vulnerable urban areas by measuring multiple criteria that are pertinent to urban communities. A total of twelve sub-criteria were selected based on literature review, local urban and environmental indicators and data availability. The Delphi technique was applied to confirm the suitability of criteria for the vulnerability indicators. The sub-criteria were categorised into four major domains: environment, urban, building and social. 2.1. Environment Domain Environmental vulnerability indicators estimate the capability of urban communities to recover from possible risks of pollution arising from several pollution sources; this capability depends primarily on the healthiness, integrity and organisational level of a com- munity [60]. Pollution sources can be classified into two major groups: point and non-point sources of pollution. The locations of point sources of pollution, such as industrial activities, can be determined. However, point-source pollution in Iraqi cities mostly originates from distributed pollution sites, such as oil industry operations, power stations, landfill sites, brick factories and wastewater treatment plants (WWTPs). The oil industry sector is a key environmental pollution source in Iraq; it releases polluting gases that affect residential neighbourhoods close to or in buffer zones [61]. WWTPs can be hotspots for the spread of antibiotic-resistant pathogens with significant effects on water ecosystems. In addition, weapon storage sites in which depleted uranium was used during the wars have continued to be tremendously harmful to public health and to Iraq’s environment since the conflict period. By contrast, non-point sources of pollution are more difficult to determine and require more effort to control. Many sites release polluting materials simultaneously [62]. The current study applied local environmental standards (specifically, number 3-2011) that have been adopted by the Iraqi Ministry of Environment. These standards determine buffer zones with different radii based on the degree of pollution. Residential neighbour- hoods located inside buffer zones are considered as urban areas exposed to pollution risks. The local environmental standards classify point-source pollution into three categories, as described below. 2.1.1. Class A: High-Polluting Projects This category includes many polluting projects, such as oil refineries, iron industries, WWTPs, brick factories, thermal power plants and landfill sites. Table 1 lists some types of high-polluting projects with their respective buffer zones based on the classification of local environmental indicators in Iraq. Table 1. Samples of high-polluting projects (Class A). Activity Types Buffer Zone Radius (km) Dangerous landfill 15 Oil refinery 10 Gas plant 10 Aluminium and cable factories 10 Thermal power station 5 Iron plant 5 Brick factory 5 Protein feed factory 3 Asphalt plant 5 Landfill 2 WWTPs 2 Earth 2022, 3 703 2.1.2. Class B: Moderately Polluting Projects This class involves polluting projects that affect the environment less than Class A projects, such as the poultry industry, plastic manufacturers, gas turbine power plants, concrete manufacturers, flour mills and date canning factories. Table 2 lists several types of moderately polluting projects with their respective buffer zone radii based on Iraqi environment indicators. Table 2. Examples of moderately polluting projects (Class B) according to Iraqi environment standards. Activity Types Buffer Zone Radius (m) Flour mill processing plant 1000 Gas power plant 1000 Wire plant 1000 Poultry industry 1000 Poultry slaughter 1000 Sandwich panel industry 1000 Woolen textile factory 500 Concert plant 500 Plastic and paint plant 500 2.1.3. Class C: Low-Polluting Projects This class includes polluting projects that affect the environment less than Class B projects, such as wastewater pumping stations, oil stores and industrial complexes. Table 3 presents some types of low-polluting projects and their respective buffer zones based on Iraqi environmental indicators. Table 3. Examples of low-polluting projects (Class C) according to Iraqi environment standards. Activity Types Buffer Zone Radius (m) Site of oil stores 500 Vehicle industrial complex 500 Pumping station of wastewater 20 2.1.4. Effects of Weapons and War A number of major international reports have confirmed that unconventional weapons used during the Gulf wars (1991–2003) were among the primary reasons for an increase in cancerous diseases in Iraq [62,63]. Large amounts of depleted uranium (DU) were fired during the Iraq wars [64]. DU has increased environmental pollution dangerously due to effects that appeared after the wars [65,66]. About 300 tons of DU were fired in the first Gulf war and about 1700 tons were fired during the 2003 war [67]. Reports have confirmed that radioactive materials (DU) that were routinely stored in military bases located close to Nasiriyah City, i.e., the study area, have leaked into the environment [68]. The most danger- ous site (the Khamisiyah site) in which chemical weapons and DU were stored is located 17 km from the border of the study area [69,70]. Radioactive emissions have permeated into the surroundings, and, as a result, people have been exposed to their dangerous effects [13]. In the current study, the effect of weapon use was defined as a polluting factor within the environmental domain. Thus, an evaluation of the effects of weapons on the environment in the study area was performed according to Iraqi environment standards, which contributed to determining the buffer zone for dangerous landfills, detailed in Table 1. Earth 2022, 3 704 2.2. Building Domain Statistical analysis conducted using quantitative and qualitative indices has shown that the vulnerability of an urban environment is primarily linked to financial resources, authority policies and city size [71]. As urbanisation continues to accelerate due to rapid population growth in Iraq, the problems arising in urban communities are becoming more complex. City authorities do not have sufficient financial and technical capabilities to provide all city neighbourhoods with basic infrastructure, such as paved streets and sewage networks. The Nasiriyah City administration is unable to control the rapid sprawl, and informal settlements involving illegal construction have continuously increased to accommodate the accelerating population growth. The informal settlements are a source of environmental pollution and a reason for the increasing number of vulnerable urban areas in Iraq. The most dangerous consequence of informal construction is the lack of proper services, such as construction of unpaved roads, which are considered a significant source of dust pollution, and the lack of public sewer and solid waste treatment systems [72]. Urban areas can be defined as vulnerable areas based on construction characteristics, particularly the infrastructure, shape and density of a settlement and its location [23]. Two sub-criteria were adopted in the current study to define vulnerable neighbourhoods within the building domain: (1) the ratio of informal settlements, and (2) the lack of infrastructure at the neighbourhood scale. 2.3. Urban Domain The most important impact factors in urban planning are urban density, population density and green public spaces, which are directly or indirectly related to vulnerability in- dicators. The well-being of urban communities is central to consideration of how the urban landscape, building density and open spaces can be utilized to address urban sprawl [73]. The integration of population and dwelling density maps enables the identification of neighbourhoods with high population density and low basic services in which mitigation interventions are urgently required [10]. In the current study, vulnerability indicators, including population density, dwelling density and green area, were classified under the urban domain to define vulnerable urban areas. 2.4. Social Domain A body of previous research has defined social vulnerability as the vulnerability of people or neighbourhoods. Social vulnerability, as a concept, has been used to characterise the capacity to control hazards and their consequences for urban communities, social groups and families [60]. Social vulnerability assessment has focused on understanding the factors associ- ated with social inequality that increase vulnerability at family and community scales [74]. The provision of health care and educational services and the availability of job opportunities are significant social indicators that can indicate the social vulnerability of urban communi- ties [9,75,76]. Therefore, data for three criteria, namely, health care services, education services and unemployment ratio, were collected in the current study to define social vulnerability indicators consistent with the local urban planning indicators of Iraq (Table 4). Table 4. Iraqi urban planning standards that refer to access distance to health care centres and schools, along with the size of social services required based on the number of people. Maximum Access Distance Facility from Dwellings to Facility Number of Units/Population (m) Nursery 300 1 per 2400–3600 capita Kindergarten 300 1 per 2400–3600 capita Primary school 500 1 per 2400–3600 capita Intermediate school 500 1 per 9600–14,400 capita Secondary school 800 1 per 9600–14,400 capita Earth 2022, 3 705 Earth 2022, 3, 7 Table 4. Cont. Maximum Access Distance Intermediate school 500 1 per 9600–14400 capita Facility from Dwellings to Facility Number of Units/Population Secondary school 800 1 per 9600–14400 capita (m) Health centre 800 1 per 9600–14400 capita Health centre 800 1 per 9600–14,400 capita Open space / 6.25 m per capita Open space / 6.25 m per capita Streets / 11.6% to 26% from total area Streets / 11.6% to 26% from total area Population density per / 250 persons per hectare (p/h) Population density per hectare / 250 persons per hectare (p/h) hectare Housing density / 42 dwellings per hectare (d/h) Housing density / 42 dwellings per hectare (d/h) 3. Method 3. Method 3.1. Study Area 3.1. Study Area Nasiriyah City was selected as the study area for this research. It represents Iraqi Nasiriyah City was selected as the study area for this research. It represents Iraqi cities because Iraq’s urban characteristics are quite similar across the whole area. Nasiri- cities because Iraq’s urban characteristics are quite similar across the whole area. Nasiriyah yah City is located along the banks of the Euphrates River, between latitudes 31°90′00″ N 0 00 City is located along the banks of the Euphrates River, between latitudes 31 90 00 N and and 30°50′00″ N, and between longitudes 46°00′00″ E and 46°20′00″ E, as shown in Figure 0 00  0 00  0 00 30 50 00 N, and between longitudes 46 00 00 E and 46 20 00 E, as shown in Figure 1. The 1. The average elevation is about 4 m above mean sea level, and its area is more than 46,000 average elevation is about 4 m above mean sea level, and its area is more than 46,000 hectares. hectares. The total population of over 700,000 people (based on the 2021 local census) cur- The total population of over 700,000 people (based on the 2021 local census) currently occupy rently occupy 92 neighbourhoods. The study area covered the Ur archaeological site (4000 92 neighbourhoods. The study area covered the Ur archaeological site (4000 BCE), as shown BCE), as shown in Figure 1. Nasiriyah City is the capital of Dhi Qar Province. The city has in Figure 1. Nasiriyah City is the capital of Dhi Qar Province. The city has suffered from the suffered from the severe effects of wars. The most dangerous site, i.e., the Khamisiyah site, severe effects of wars. The most dangerous site, i.e., the Khamisiyah site, where chemical where chemical weapons and depleted uranium (DU) were used, is located about 17 km weapons and depleted uranium (DU) were used, is located about 17 km from its borders. from its borders. Figure 2 shows the location of this site [69,70]. This dangerous site has Figure 2 shows the location of this site [69,70]. This dangerous site has become closer to the become closer to the city settlements due to rapid urban sprawl, high population growth, city settlements due to rapid urban sprawl, high population growth, migration towards the migration towards the city and poor urban planning, resulting in the establishment of city and poor urban planning, resulting in the establishment of large informal settlements in large informal settlements in the study area the study area. Figure 1. Location of Nasiriyah City. Upper left: location of Iraq on the world map. Bottom left: location of Nasiriyah City on the Iraq map. Right: map of Nasiriyah City showing the location of the ancient city of Ur (4000 BC). The brown area comprises 92 occupied neighbourhoods. Earth 2022, 3, 8 Figure 1. Location of Nasiriyah City. Upper left: location of Iraq on the world map. Bottom left: location of Nasiriyah City on the Iraq map. Right: map of Nasiriyah City showing the location of Earth 2022, 3 706 the ancient city of Ur (4000 BC). The brown area comprises 92 occupied neighbourhoods. Figure 2. Khamisiyah site and boundary of Nasiriyah City. The three pictures on the right show a Figure 2. Khamisiyah site and boundary of Nasiriyah City. The three pictures on the right show a close-up view of the Khamisiyah site. close-up view of the Khamisiyah site. 3.2. Data Collection 3.2. Data Collection In this study, 12 dataset layers were collected to identify the vulnerable urban areas in In this study, 12 dataset layers were collected to identify the vulnerable urban areas Nasiriyah City. These layers were as follows: high-polluting sources, moderately polluting in Nasiriyah City. These layers were as follows: high-polluting sources, moderately pol- sources, low-polluting sources, DU landfill, informal settlement rate, lack of infrastructure, luting sources, low-polluting sources, DU landfill, informal settlement rate, lack of infra- housing density, population density, green space, health care service size, education service structure, housing density, population density, green space, health care service size, edu- size and unemployment rate. They were categorised into four major domains: environment, cation service size and unemployment rate. They were categorised into four major do- building, urban and social, as shown in Figure 3. In addition, land surveying was conducted mains: environment, building, urban and social, as shown in Figure 3. In addition, land to obtain accurate results by utilising global positioning system (GPS) instruments. Table 5 surveying was conducted to obtain accurate results by utilising global positioning system describes the datasets used in this study. (GPS) instruments. Table 5 describes the datasets used in this study. Table 5. Types, description and accuracy of the data used in this study. Table 5. Types, description and accuracy of the data used in this study. No. Data Description Source Accuracy No. Data Description Source Accuracy It was used to classify the land It was used to classify the land cover European Union’s Earth European Union’s Sentinel 2 cover of the study area and extract Sentinel 2 of the study area and extract green 1 observation programme 10 m 1 image, October 2021 green space (sub-criteria of the Earth observation pro- 10 m (Copernicus) image, October 2021 space (sub-criteria of the urban do- urban domain). gramme (Copernicus) main). The images were utilised to The images were utilised to validate validate land use classes and the Pléiades 1, product name: Pléiades 1, product name: Iraqi General Survey land use classes and the boundary of Iraqi General Survey 2 boundary of old neighbourhoods 0.50 m ORTHO, 2014 Authority 2 ORTHO, 0.50 m and to increase the resolution of old neighbourhoods and to increase Authority image classification. the resolution of image classification. The data were analysed spatially The data were analysed spatially to Office of the Munici- Land use—streets, districts to classify land use classes, street 3 classify land use classes, street case pality of Nasiriyah 2 m Land use—streets, districts case (asphalt or dusty) and Office of the Municipality and water networks, 2021 3 2 m (asphalt or dusty) and wastewater City, Iraq and water networks, 2021 wastewater discharge systems of Nasiriyah City, Iraq (sewage network systems or home septic tanks). Earth 2022, 3 707 Table 5. Cont. No. Data Description Source Accuracy The shape files were analysed to Master plan of compare actual land use with the Office of Urban Planning, 4 2 m Nasiriyah City master plan of the city based on Nasiriyah City, Iraq urban planning indicators. They were manipulated spatially to determine the locations of Pipeline wastewater, polluted sources (WWTPs) and manhole sewages, pump Office of Sewage 5 pump stations of wastewater. 2 m stations and water Department in Dhi Qar Spatial analysis of infrastructure treatment stations (WTSs) distribution in the city was conducted. They were treated spatially and Poultry sites, protein then entered within the Agriculture Directorate of 6 5 m factories and animal feeds sub-criteria of the Dhi Qar, Iraq environment domain. They were analysed spatially and listed under point-source pollution Dhi Qar Investments 7 Polluted industrial projects 5 m (sub-criteria of the Office (Iraq) environment domain). They were manipulated and integrated with spatial data and Dhi Qar Environment 8 Polluted sites (2021) 1 m then organised under Office (Iraq) point-source pollution. The data were analysed spatially and then compared with urban Health care centres and Ministry of Health 9 planning indicators before being 2 m hospitals (2021) (Dhi Qar office, Iraq) entered into the sub-criteria of the social domain. The same processes in Item (9) Ministry of Education 10 Schools (2021) 2 m were performed. (Dhi Qar office, Iraq) Data were entered into the Iraqi Ministry of Planning, Neighbourhood 11 Unemployment rate (2021) sub-criteria of the social domain. Department of Statistics scale They were converted into raster form and then utilised to validate 1/25,000 Office of Urban Planning, 12 Paper maps (2020) the image classification and spatial 1/10,000 Nasiriyah City, Iraq distribution pattern digitisation of 1/2500 missed geographic features. Population housing Data were entered as sub-criteria Ministry of Neighbourhood census (2021) of the urban domain. Planning/Statistics Office scale The work was required to validate Site survey using data, increase the resolution of the 14 Author 2 m GPS (2022) geographic features of locations and complete missing data. They were used for the Site survey using drone 15 digitalisation of informal Author 2 m images (February 2021) settlements. 3.3. GIS Database Design and Management A geodatabase was designed by applying various GIS operations. These procedures were applied to vector data versus raster data, which differed in structure. Raster data contain equal-sized cells that form a continuous surface. Vector data comprise polygons, lines and points that form distinct geographic features on Earth. In addition, spatial and Earth 2022, 3, 10 Earth 2022, 3 708 lines and points that form distinct geographic features on Earth. In addition, spatial and textual data were integrated into the geodatabase. Subsequently, the sub-criteria relevant textual data were integrated into the geodatabase. Subsequently, the sub-criteria relevant to vulnerability were extracted and then categorised into four major criteria: environment, to vulnerability were extracted and then categorised into four major criteria: environment, building, urban and social. Figure 3 shows the layers of the sub-criteria that were required building, urban and social. Figure 3 shows the layers of the sub-criteria that were required for running the MCDA to define vulnerable urban areas. for running the MCDA to define vulnerable urban areas. Figure 3. Figure 3.F Flowchart lowchart of ofdata dataco collection llection and and cla classification ssification of of the criter the criteria ia and and s sub-criteria. ub-criteria. 3.4. Delphi Technique 3.4. Delphi Technique The criteria and sub-criteria that were defined based on the literature review were The criteria and sub-criteria that were defined based on the literature review were reviewed by an expert panel using the Delphi method to confirm the criteria that were the reviewed by an expert panel using the Delphi method to confirm the criteria that were most relevant to the vulnerability indicators. Delphi is an expert judgment technique in the most relevant to the vulnerability indicators. Delphi is an expert judgment technique which a in whichgroup of well-kno a group of well-known wn experts in a spec experts in a specific ific field expre field expr ss t ess heir opin their opinions ions durin during g a series of discussions by following a prepared questionnaire to arrive at the group’s opin- a series of discussions by following a prepared questionnaire to arrive at the group’s ions opinions about about a specif a specific ic issue issue [77]. An expe [77]. Anrt expert panel w panel as care was fu car llyefully selected. It selected. cons It isconsisted ted of 22 qu ofalified 22 qualified experts, expe sixrts, expe six rts from experts the e from nvi the ronment doma environmentin domain, , ten experts ten experts from the from urba the n plannin urban planning g department department and six experts fr and six experts om the con from s the truction constr domain. The uction domain. expert The s p experts artici- participated in multiple meetings with the purpose of integrating viewpoints into a group pated in multiple meetings with the purpose of integrating viewpoints into a group con- consensus. After each round, the answers were summarised and transferred to the experts. sensus. After each round, the answers were summarised and transferred to the experts. The experts were allowed to modify their responses in the next rounds, depending on how The experts were allowed to modify their responses in the next rounds, depending on they analysed the group opinion. The result of this method was that nearly all the criteria how they analysed the group opinion. The result of this method was that nearly all the and sub-criteria were approved as relevant to urban vulnerability indicators. Figure 3 criteria and sub-criteria were approved as relevant to urban vulnerability indicators. Fig- shows the major criteria and sub-criteria endorsed by the expert panel. ure 3 shows the major criteria and sub-criteria endorsed by the expert panel. 3.5. Spatial Analysis Processes 3.5. Spatial Analysis Processes After the collected data were organised into four primary datasets (urban, building, After the collected data were organised into four primary datasets (urban, building, social and environment), two types of spatial analysis technique (continuous and discrete) social and environment), two types of spatial analysis technique (continuous and discrete) were performed according to the type of data before a weighted linear combination func- were performed according to the type of data before a weighted linear combination func- tion (WLC) was applied to produce vulnerability indicators for each domain, as shown tion (WLC) was applied to produce vulnerability indicators for each domain, as shown in in Figure 4. Figure 4. Earth 2022, 3, 11 Earth 2022, 3 709 Figure 4. Flowchart of the methods used to produce vulnerability indicators: environment (Ve), Figure 4. Flowchart of the methods used to produce vulnerability indicators: environment (Ve), ur- urban (Vu), building (Vb) and social (Vs). ban (Vu), building (Vb) and social (Vs). 3.5.1. Spatial Analysis of Continuous Data 3.5.1. Spatial Analysis of Continuous Data In this study, data belonging to the environment domain was continuous. Given that In this study, data belonging to the environment domain was continuous. Given that all polluting projects and the DU landfill represented point sources of pollution (PSPs), their all polluting projects and the DU landfill represented point sources of pollution (PSPs), effects could be continuous across the study area to a different degree based on distance their effects could be continuous across the study area to a different degree based on dis- from the source. Therefore, two sequential operations, namely, Euclidean distance and tance from th FLM, were conducted e source. The befor refore e the, two se sub-criteria quen wer tial operations, namely e weighted with values , Euc obtained lidean distanc from e FAHP. Then, an FO analysis was performed on the major criteria to obtain the final fuzzy and FLM, were conducted before the sub-criteria were weighted with values obtained map of total vulnerability. from FAHP. Then, an FO analysis was performed on the major criteria to obtain the final fuzzy map of total vulnerability. 1. Euclidean distance function 1. Euclidean distance function Euclidean distance is a spatial analysis function available in the GIS environment. It uses the Pythagorean theorem to calculate the Euclidean distance to the closest source Euclidean distance is a spatial analysis function available in the GIS environment. It for each cell based on Formula (1). Through this function, the vector layer dataset that uses the Pythagorean theorem to calculate the Euclidean distance to the closest source for belonged to the environment domain was converted into raster form that indicated the each cell based on Formula (1). Through this function, the vector layer dataset that be- existing distances from the pollution source to the remaining buffer area. longed to the environment domain was converted into raster form that indicated the ex- isting distances from the pollution source to the remaining buffer area. h i 2 2 d = (X2 X1) + (y2 y1) (1) ( ) ( ) (1) 𝑑 = 𝑋2 − 𝑋1 + 𝑦2 − 1 𝑦 where d represents the distance between a pollution source and the remaining points. where d represents the distance between a pollution source and the remaining points. 2. Fuzzification 2. Fuzzification Whilst FL emulates human logic by using artificial intelligence (AI) techniques, only Whilst FL emulates human logic by using artificial intelligence (AI) techniques, only two options are restricted in the Boolean logic (BL) of computers: 0 or 1 [78]. FL allows for a two options are restricted in the Boolean logic (BL) of computers: 0 or 1 [78]. FL allows for degree of contribution to the reaction, represented by a membership function [49]. Although a degree of contribution to the reaction, represented by a membership function [49]. Alt- classical theory is founded on crisp sets, according to which each indicator belongs to a hough classical theory is founded on crisp sets, according to which each indicator belongs quite-determined class, FL access evaluates the different degrees of membership of each to a quite-determined class, FL access evaluates the different degrees of membership of indicator to various classes. This approach has been used in contemporary research by each indicator to various classes. This approach has been used in contemporary research examining a complex topic affected by uncertainty, such as vulnerability to climate change by examining a complex topic affected by uncertainty, such as vulnerability to climate and urbanisation [7]. A negative FLM function was used to standardise the sub-criteria of change and urbanisation [7]. A negative FLM function was used to standardise the sub- the environment domain (effects of Classes A, B and C and DU) into fuzzy values between criteria of the environment domain (effects of Classes A, B and C and DU) into fuzzy val- ues between 0 and 1 based on Formula (2). Figure 5 illustrates negative and positive FLM functions, i.e., Formula (3). Earth 2022, 3, 12 Earth 2022, 3 710 1 𝑥 < 0 0 and 1 based on Formula (2). Figure 5 illustrates negative and positive FLM functions, i.e., (𝑀𝑎𝑥 − 𝑥) Formula (3). ( ) 𝑚𝑖𝑛 < 𝑥 < 𝑚𝑎 𝑓 𝑥 = 8 (2) (𝑀𝑎𝑥 − 𝑚) 1 x < 0 0 𝑖𝑓 𝑥 > 𝑚𝑎𝑥 (Max x) f (x) = min < x < ma (2) > (Max min) 1 𝑜𝑟 0 𝑥 > 𝑚𝑎𝑥 0 i f x > max 0 𝑜𝑟 1 𝑥 < 𝑚𝑖𝑛 ( ) 𝑓 𝑥 = (3) 8 (𝑋 − ) 1 or 0 x 𝑚𝑖> 𝑛 <m 𝑥a x < 𝑚𝑎 (𝑀𝑎𝑥 − 𝑖𝑛𝑀 ) 0 or 1 x < min f (x) = (3) (X Min) : min < x < ma (Max Min) Figure 5. The yellow line represents a negative FLM function based on Formula (2), whilst the blue line represents a positive FLM function based on Formula (3). 3.5.2. Methods for Processing and Analysing Discrete Data Figure 5. The yellow line represents a negative FLM function based on Formula (2), whilst the blue line represents a positive FLM function based on Formula (3). Data belonging to the urban, building and social domains are discrete data. The collected data were distributed inside the boundaries of neighbourhoods based on actual 3.5.2. Methods for Processing and Analysing Discrete Data quantities. The collected data related to the sub-criteria were manipulated, and spatial analysis Data bel was onging to the urb performed within an, buil the boundaries ding and soc of the ial do neighbour mains are hoods. discrete d Therefor ata. Th e, discr e col- ete lected data wer date a w converted ere distr into ibute raster d insid form e the using boundar the rasteri ies o sation f neighbo function urhoods b and then asenormalisation d on actual was applied to standardise the original values of the sub-criteria. quantities. The collected data related to the sub-criteria were manipulated, and spatial analysis was performed within the boundaries of the neighbourhoods. Therefore, discrete 1. Rasterisation data were converted into raster form using the rasterisation function and then normalisa- Eight raster layers were generated from the shape file form of the sub-criteria belonging tion was applied to standardise the original values of the sub-criteria. to the three domains (building, urban and social) based on specific values, as shown 1. Rasterisation in Figure 6. Eight raster layers were generated from the shape file form of the sub-criteria belong- ing to the three domains (building, urban and social) based on specific values, as shown in Figure 6. 𝑀𝑖𝑛 𝑖𝑛 Earth 2022, 3 711 Earth 2022, 3, 13 Earth 2022, 3, 13 Figure 6. Conversion of a shape file into raster form using the rasterisation tool. Figure 6. Conversion of a shape file into raster form using the rasterisation tool. Figure 6. Conversion of a shape file into raster form using the rasterisation tool. 2. Normalisation 2. Normalisation 2. Normalisation In quantitative studies with various data sources, such as the current study, stand- In quantitative studies with various data sources, such as the current study, standard- In quantitative studies with various data sources, such as the current study, stand- isation ardisatis ior n equir is reed quito red make to mmeaningful ake meaning comparisons ful compariso on nsthe on t basis he ba of sis values of valu measur es meaed sur in ed ardisation is required to make meaningful comparisons on the basis of values measured dif in di ferent fferent units un [79 its [ ]. Ther 79]. Th efor er e, efore normalising , normalis the ing original the original v values a of lues theof the gener generated raster ated raster from in different units [79]. Therefore, normalising the original values of the generated raster the from t previous he prev step ious was step wa necessary s necess toary permit to permit expressive exprescomparisons. sive comparisons. A All original ll origivalues nal val- from the previous step was necessary to permit expressive comparisons. All original val- were converted between 0 and 1 in this step based on the linear interpolation equation, i.e., ues were converted between 0 and 1 in this step based on the linear interpolation equation, ues were converted between 0 and 1 in this step based on the linear interpolation equation, Formula i.e., Form (4). ula Whilst (4). Wh number ilst number one ind one indicatesicat extr es eme extreme vulner vulnerability abilit , zero y, ze indicates ro indiminimum cates mini- i.e., Formula (4). Whilst number one indicates extreme vulnerability, zero indicates mini- vulnerability. Therefore, the increment in value is associated with an increase in potential mum vulnerability. Therefore, the increment in value is associated with an increase in mum vulnerability. Therefore, the increment in value is associated with an increase in vulnerability for a specific sub-criterion, as shown in Figure 7. potential vulnerability for a specific sub-criterion, as shown in Figure 7. potential vulnerability for a specific sub-criterion, as shown in Figure 7. 𝑥𝑖 − 𝑥𝑖. 𝑚𝑖𝑛𝑚𝑢𝑚 xi xi.minmum 𝑦𝑖 = (4) 𝑥𝑖 − 𝑥𝑖. 𝑚𝑖𝑛𝑚𝑢𝑚 yi = (4) 𝑥𝑖. 𝑢𝑚𝑚𝑎𝑥𝑖𝑚 − 𝑥𝑖. 𝑚𝑚𝑖𝑛𝑖𝑚𝑢 (4) 𝑦𝑖 = xi.maximum xi.minimum 𝑥𝑖. 𝑢𝑚𝑚𝑎𝑥𝑖𝑚 − 𝑥𝑖. 𝑚𝑚𝑖𝑛𝑖𝑚𝑢 where 0 ≤ yi ≥ 1,and (xi) is the value of any raster cell. where 0  yi  1,and (xi) is the value of any raster cell. where 0 ≤ yi ≥ 1,and (xi) is the value of any raster cell. Figure 7. Normalisation process. Figure 7. Normalisation process. Figure 7. Normalisation process. 3.6. AHP and FL 3.6. AHP and FL 3.6. AHP and FL AHP is a technique used to evaluate a group of factors, criteria or activities that affect AHP is a technique used to evaluate a group of factors, criteria or activities that AHP is a technique used to evaluate a group of factors, criteria or activities that affect a specific phenomenon to varying degrees [80]. Although AHP was proposed in the 1980s, affect a specific phenomenon to varying degrees [80]. Although AHP was proposed in the a specific phenomenon to varying degrees [80]. Although AHP was proposed in the 1980s, it remains an essential analysis method for subjects involving many options when per- 1980s, it remains an essential analysis method for subjects involving many options when it remains an essential analysis method for subjects involving many options when per- forming a pairwise comparison of the options is difficult. FAHP is an enhanced version of performing a pairwise comparison of the options is difficult. FAHP is an enhanced version forming a pairwise comparison of the options is difficult. FAHP is an enhanced version of AHP that supports a methodical alternative choosing rationale [43]. The traditional AHP of AHP that supports a methodical alternative choosing rationale [43]. The traditional AHP AHP that supports a methodical alternative choosing rationale [43]. The traditional AHP method is accompanied by uncertainty because of crisp value judgements; thus, it does method is accompanied by uncertainty because of crisp value judgements; thus, it does method is accompanied by uncertainty because of crisp value judgements; thus, it does not reflect human reasoning. Accordingly, FAHP was ultimately used to address this is- not reflect human reasoning. Accordingly, FAHP was ultimately used to address this issue not reflect human reasoning. Accordingly, FAHP was ultimately used to address this is- sue and achieve a more confident decision. The two methods were selected and used in and achieve a more confident decision. The two methods were selected and used in the sue and achieve a more confident decision. The two methods were selected and used in the current research. The AHP method was first applied to organise the hierarchical form current research. The AHP method was first applied to organise the hierarchical form and the current research. The AHP method was first applied to organise the hierarchical form and calculate the consistency ratio (CR) when investigating the consistency degree be- calculate the consistency ratio (CR) when investigating the consistency degree between and calculate the consistency ratio (CR) when investigating the consistency degree be- tween the weights of different values. Subsequently, the FAHP technique was used to the weights of different values. Subsequently, the FAHP technique was used to obtain the tween the weights of different values. Subsequently, the FAHP technique was used to Earth 2022, 3 712 criterion weights of the major domains (environment, urban, building and social) and the sub-criteria according to the following sequential steps: 1. Creating a pairwise comparison matrix. A pairwise comparison matrix was prepared based on the questionnaire survey results. Nine experts compared the relevant criteria with vulnerability indicators. The related weights of these criteria based on AHP were computed. To examine the consistency grade between the weighted values of various parameters, CR was calculated using the three formals (5)–(7). The results showed that the CR values were less than 0.1; thus, the pairwise comparison matrices were suitable. a  w 1 ij i = , (5) max n w i=1 where (a ) is a pairwise comparison matrix element, and (w ) is the weight value of ij i each parameter. ( n) max CI = (6) n 1 ( ) CI CR = (7) RI CI denotes the consistency index, whilst RI represents the mean of the random index that was calculated in accordance with Saaty’s rating RI (1–10) [81]. 2. The comparative importance hierarchy values are crisp in AHP. Thus, crisp values were transformed into fuzzy numbers in this step based on the triangular fuzzy mem- bership equation, i.e., Formula (8). Fuzzy value is described by three determinations {a, b, c}, as illustrated in Figure 8. 0, x < a > x a a  x  b b a m = (8) traingle(x) c x > b  x  c c b 0, x > c 3. In this step, the fuzzy geometric mean value (er) of every criterion was calculated using Formula (9). n o 1/n n n e e e e e e er = (A ) = A  A  A  A . . . A (9) i Õ ij i1 i2 i3 i4 in j=1 4. The fourth step was the determination of the fuzzy comparative weight of each criterion, as follows: we = er  (er +er +er +er + . . . er ) (10) i i 1 2 3 4 where (A ) is a fuzzy comparison matrix of dimension i to criterion j. ij 5. Determining the weights of the crisp values using the centre of area (COA) method based on Formula (11). (Lwe + M we + U we ) i i i w = (11) 6. The final step was the standardisation of the relative weights (w ) by applying Formula (12), and lastly, collecting the final weight (W ). Table 6 provides the results. ni W = , w = 1, w > 0 (12) ni å i i i=1 Earth 2022, 3, 14 obtain the criterion weights of the major domains (environment, urban, building and so- cial) and the sub-criteria according to the following sequential steps: 1. Creating a pairwise comparison matrix. A pairwise comparison matrix was prepared based on the questionnaire survey results. Nine experts compared the relevant crite- ria with vulnerability indicators. The related weights of these criteria based on AHP were computed. To examine the consistency grade between the weighted values of Earth 2022, 3 713 various parameters, CR was calculated using the three formals (5, 6 and 7). The re- sults showed that the CR values were less than 0.1; thus, the pairwise comparison matrices were suitable. Table 6. Results of the FAHP method. 1 𝑎 × 𝑤 λ = , (5) Partial Weight = Sub-Weight Major Criteria Major Weight Sub-Criteria Sub-W 𝑛 eight 𝑤 Major Weight Class A (c1) 0.221 0.097 where (aij) is a pairwise comparison matrix element, and (wi) is the weight value of each parameter. Class B (c2) 0.128 0.057 Environment domain (A) (λ −𝑛 ) 0.441 Class C (c3) 0.076 0.034 𝐶𝐼 = (6) ( ) 𝑛− 1 Weapon effects (c4) 0.575 0.254 𝐶𝐼 Subtotal 1.000 0.441 (7) 𝐶𝑅 = Informal settlements (c5) 0.644 0.175 CI denotes the consistency index, whilst RI represents the mean of the random index Building domain (B) Lack of infrastructure (c6) 0.356 0.097 that was calculated in accordance with Saaty’s rating RI (1–10) [81]. Subtotal 1.000 0.271 2. The comparative importance hierarchy values are crisp in AHP. Thus, crisp values Population density (c7) 0.491 0.071 were transformed into fuzzy numbers in this step based on the triangular fuzzy Housing density (c8) 0.255 0.037 membership equation, i.e., Formula (8). Fuzzy value is described by three determi- Urban domain (C) 0.144 Green space (c9) 0.255 0.037 nations {a, b, c}, as illustrated in Figure 8. Subtotal 1.000 0.144 0, 𝑥 < 𝑎 Health services (c10) 0.255 0.037 𝑥−𝑎 𝑎 𝑥 𝑏 Educational services (c11) 0.255 0.037 Social domain (D) 0.144 𝑏−𝑎 (8) Unemployment µ rate (c12) = 0.491 0.071 () 𝑐− 𝑥 𝑏 𝑥 𝑐 Subtotal 1.000 0.144 𝑐−𝑏 Total = 1 = 1 1.000 å å 0, 𝑥 > 𝑐 Figure 8. Triangular fuzzy membership equation. Figure 8. Triangular fuzzy membership equation. 3.7. WLC 3. In this step, the fuzzy geometric mean value (r˜) of every criterion was calculated Using WLC, the acquired weights from FAHP were entered on the basis of Formula (13) using Formula (9). to aggregate each group of sub-criteria into a single layer [82,83]. The results were four individual vulnerability maps: environmental vulnerability (V ), building vulnerability (V ), social vulnerability (V ) and urban vulnerability (V ), as shown in Figure 4. s u V = c w (q ) (13) e,b,u,s å i i i i=0 where n is the number of sub-criteria, (w ) is the relative weight of a sub-criterion (c ) and i i (q ) is the amount of a sub-criterion (c ). i i 𝑅𝐼 Earth 2022, 3, 16 where n is the number of sub-criteria, (wi ) is the relative weight of a sub-criterion (ci) and Earth 2022, 3 714 (qi) is the amount of a sub-criterion (ci). 3.8. Final Fuzzy Map 3.8. Final Fuzzy Map 3.8.1. Aggregated Vulnerability (Va) 3.8.1. Aggregated Vulnerability (Va) In accordance with the theory of vulnerability, the aggregated vulnerability (Va) was In accordance with the theory of vulnerability, the aggregated vulnerability (Va) was taken to be the product of the three vulnerability domains: urban, social and building, Va taken to be the product of the three vulnerability domains: urban, social and building, = Vu. Vs.Vb [31]. FL was used to consider uncertainty in the classification and combination Va = Vu  Vs  Vb [31]. FL was used to consider uncertainty in the classification and com- of the vulnerability indicators. Similarly, the FO tool was used to examine the potential of bination of the vulnerability indicators. Similarly, the FO tool was used to examine the an event relevant to various sets in a multi-criterion overlay examination. Although FO potential of an event relevant to various sets in a multi-criterion overlay examination. specifies which sets a phenomenon is possibly a member of, it also examines the relation- Although FO specifies which sets a phenomenon is possibly a member of, it also examines ships amongst members of various sets [49]. The FO tool was applied twice (fuzzy product the relationships amongst members of various sets [49]. The FO tool was applied twice and fuzzy sum) to obtain the best indicator of vulnerability by calculating FOG using For- (fuzzy product and fuzzy sum) to obtain the best indicator of vulnerability by calculating mula (14), as shown in Figure 9. FOG using Formula (14), as shown in Figure 9. ( ) ( ) 1g g n n 𝑉 = 𝑓 . 1 − (1 − 𝑓 ) (14) , () ( ) V = f . 1 1 f (14) a,t Õ (c) Õ (c) ; ; c;1 c;1 where n is the number of input rasters, f is the value of the pixel of each input raster, where n is the number of input rasters, f(c) is the value of the pixel of each input raster, γ (c) g is gamma (0.90) and V is the fuzzy gamma map of aggregation vulnerability and is gamma (0.90) and Va,t is the fuzzy gamma map of aggregation vulnerability and com- a ,t comprehensive vulnerability. prehensive vulnerability. Figure 9. Figure 9. FOG FOG was use was used d to to aggregate the thr aggregate the three vulnerabi ee vulnerability lity indi indicators cators (u (urban, rban, socia social l and and bu building) ilding) into a single indicator, called the aggregated vulnerability Indicator. into a single indicator, called the aggregated vulnerability Indicator. 3.8.2. Comprehensive Vulnerability Maps (Vt) 3.8.2. Comprehensive Vulnerability Maps (Vt) The final fuzzy map presents a comprehensive vulnerability map of the study area The final fuzzy map presents a comprehensive vulnerability map of the study area that was produced by multiplying the environment vulnerability indicators (Ve) by the that was produced by multiplying the environment vulnerability indicators (Ve) by the aggregated vulnerability indicators (Va) based on vulnerability theory, in accordance with aggregated vulnerability indicators (Va) based on vulnerability theory, in accordance with Formula 15 using FOG, as shown in Figure 10. To reduce evaluation subjectivity, two Formula 15 using FOG, as shown in Figure 10. To reduce evaluation subjectivity, two dif- different scenarios of the overall vulnerability maps were proposed. In the first scenario, ferent scenarios of the overall vulnerability maps were proposed. In the first scenario, each each of the four domains (environment, urban, social and building) was weighted with the of the four domains (environment, urban, social and building) was weighted with the ob- obtained value from the FAHP method. In the second scenario, each of the four domains tained value from the FAHP method. In the second scenario, each of the four domains had had the same value. the same value. Vt = Vu Vs Vb Ve (15) (15) Vt = Vu ∗ Vs ∗ Vb ∗ Ve Earth 2022, 3 715 Earth 2022, 3, 17 Figure 10. FOG was used to aggregate the four vulnerability indicators (environment, urban, social Figure 10. FOG was used to aggregate the four vulnerability indicators (environment, urban, social and and bu building) ilding)into into a s a single ingle ind indicator icator, , called called the the com compr pehensive rehensive vu vulnerabil lnerabil ity ity indicator indicator . . 3.9. Jenks Optimisation Method 3.9. Jenks Optimisation Method To understand vulnerability maps produced in this study and to characterise the data To understand vulnerability maps produced in this study and to characterise the data visually, the Jenks natural breaks (JNB) classification technique was used to reclassify the visually, the Jenks natural breaks (JNB) classification technique was used to reclassify the numerical values of the spatial data. The JNB technique utilises an algorithm that aims to numerical values of the spatial data. The JNB technique utilises an algorithm that aims to minimise the deviation of weight in each type from the type average [84]. Furthermore, this minimise the deviation of weight in each type from the type average [84]. Furthermore, algorithm attempts to increase the deviation of weights from the average of the other types this algorithm attempts to increase the deviation of weights from the average of the other on the basis of Formula (16) [85,86]. Consequently, the vulnerability maps were reclassified types on the basis of Formula (16) [85,86]. Consequently, the vulnerability maps were re- into five classes (very high, high, medium, low and very low) to enable decision-makers to classified into five classes (very high, high, medium, low and very low) to enable decision- interpret the results easily. makers to interpret the results easily. (SDAM SDCM) GVF = (16) (SDAM − SDCM) SDAM (16) GVF = 𝑆𝐷𝐴𝑀 where GVF is between 1 and 0, and represents the goodness of fit of the different proper variables; SDAM represents the total of the squared deviations from the average of the where GVF is between 1 and 0, and represents the goodness of fit of the different proper current array; and SDCM represents the total of the squared deviations from the average of variables; SDAM represents the total of the squared deviations from the average of the each type. current array; and SDCM represents the total of the squared deviations from the average of each type. 4. Results The results were six vulnerability maps produced at the neighbourhood scale of the 4. Results study area (Nasiriyah City): 1—urban vulnerability map (Vu), 2—social vulnerability map The results were six vulnerability maps produced at the neighbourhood scale of the (Vs), 3—building vulnerability map (Vb), 4—aggregated vulnerability map (Va), comprising study area (Nasiriyah City): 1—urban vulnerability map (Vu), 2—social vulnerability map three domains (urban, social and building), 5—environmental vulnerability map (Ve), and (Vs), 3—building vulnerability map (Vb), 4—aggregated vulnerability map (Va), compris- 6—final fuzzy map (Vt), as well as overall vulnerability maps (urban, social, building ing three domains (urban, social and building), 5—environmental vulnerability map (Ve), and environment). To provide understandable vulnerability maps, JNB classification was and 6—final fuzzy map (Vt), as well as overall vulnerability maps (urban, social, building used to classify the study area into five classes, depending on the proposed vulnerability and environment). To provide understandable vulnerability maps, JNB classification was indicators, from a very high vulnerability region to a very low vulnerability region. used to classify the study area into five classes, depending on the proposed vulnerability indicators, from a very high vulnerability region to a very low vulnerability region. 4.1. Urban Vulnerability Map Based on Formula (13), an urban vulnerability map (Vu) was produced by the over- 4.1. Urban Vulnerability Map lapping of sub-criteria, namely, dwelling density (C7), population density (C8) and green Based on Formula (13), an urban vulnerability map (Vu) was produced by the over- space ratio (C9), as shown in Figure 11. In Figure 12, 13 neighbourhoods of the city are lapping of sub-criteria, namely, dwelling density (C7), population density (C8) and green shown to be located in the very high vulnerability region. More than 196,928 people, i.e., space ratio (C9), as shown in Figure 11. In Figure 12, 13 neighbourhoods of the city are 28% of the city’s total population, are located in this region. In addition, eight neighbour- shown to be located in the very high vulnerability region. More than 196,928 people, i.e., hoods with more than 89,000 people, or 12% of the total population, are located in the high 28% of the city’s total population, are located in this region. In addition, eight neighbour- vulnerability region. hoods with more than 89,000 people, or 12% of the total population, are located in the high vulnerability region. Earth 2022, 3, 18 Earth 2022, 3 716 Earth 2022, 3, 18 (a) (b) (c) (a) (b) (c) Figure 11. Sub-criteria of the urban domain: (a) dwelling density (C7), (b) population density (C8) Figure 11. Sub-criteria of the urban domain: (a) dwelling density (C7), (b) population density (C8) Figure 11. Sub-criteria of the urban domain: (a) dwelling density (C7), (b) population density (C8) and (c) green space ratio (C9). and (c) green space ratio (C9). and (c) green space ratio (C9). Figure 12. Urban vulnerability map (Vu). Figure 12. Urban vulnerability map (Vu). Figure 12. Urban vulnerability map (Vu). 4.2. Social Vulnerability Map Using the same technique, a social vulnerability map (Vs) was produced by over- lapping sub-criteria, namely, education services (C10), health care services (C11) and unemployment rate (C12), as shown in Figure 13. In the process of gathering sub-criteria, Earth 2022, 3, 19 4.2. Social Vulnerability Map Earth 2022, 3, 19 Using the same technique, a social vulnerability map (Vs) was produced by overlapping sub-criteria, namely, education services (C10), health care services (C11) and unemployment rate (C12), as shown in Figure 13. In the process of gathering sub-criteria, each cr 4.2 it.erion Social was mu Vulnerability ltiplie Map d by the relevant weight obtained from FAHP using Formula Earth 2022, 3 717 (14) to produce Vs, as shown in Figure 14. Using the same technique, a social vulnerability map (Vs) was produced by overlapping sub-criteria, namely, education services (C10), health care services (C11) and unemployment rate (C12), as shown in Figure 13. In the process of gathering sub-criteria, each criterion was multiplied by the relevant weight obtained from FAHP using Formula each criterion was multiplied by the relevant weight obtained from FAHP using Formula (14) to produce Vs, as shown in Figure 14. (14) to produce Vs, as shown in Figure 14. (a) (b) (c) Figure 13. Sub-criteria of the social domain: (a) education index (C10), (b) health care index (C11) and (c) unemployment rate (C12). As shown in Figure 14, 10 neighbourhoods of the city are located in the very high vulnerability (a) (b) (c) region. These neighbourhoods are home to more than 197,559 people, i.e., 28% of the total popula- Figure 13. Sub-criteria of the social domain: (a) education index (C10), (b) health care index (C11) tion. In addition, 18 neighbourhoods with more than 172,000 residents, i.e., about 24% of the total Figure 13. Sub-criteria of the social domain: (a) education index (C10), (b) health care index (C11) and (c) unemployment rate (C12). population, are located in the high vulnerability region. and (c) unemployment rate (C12). As shown in Figure 14, 10 neighbourhoods of the city are located in the very high vulnerability region. These neighbourhoods are home to more than 197,559 people, i.e., 28% of the total popula- tion. In addition, 18 neighbourhoods with more than 172,000 residents, i.e., about 24% of the total population, are located in the high vulnerability region. Figure 14. Social vulnerability map (Vs). Figure 14. Social vulnerability map (Vs). As shown in Figure 14, 10 neighbourhoods of the city are located in the very high vulnerability region. These neighbourhoods are home to more than 197,559 people, i.e., 28% of the total population. In addition, 18 neighbourhoods with more than 172,000 residents, Figure 14. Social vulnerability map (Vs). i.e., about 24% of the total population, are located in the high vulnerability region. 4.3. Building the Vulnerability Map The building vulnerability map (Vb) was created by overlapping two sub-criteria, i.e., informal settlement rate (C5) and the lack of infrastructure rate (C6), using the same technique mentioned earlier, as shown in Figures 15 and 16. The latter shows that three Earth 2022, 3, 20 Earth 2022, 3, 20 4.3. Building the Vulnerability Map 4.3. Building the Vulnerability Map The building vulnerability map (Vb) was created by overlapping two sub-criteria, The building vulnerability map (Vb) was created by overlapping two sub-criteria, i.e., informal settlement rate (C5) and the lack of infrastructure rate (C6), using the same i.e., informal settlement rate (C5) and the lack of infrastructure rate (C6), using the same technique mentioned earlier, as shown in Figures 15 and 16. The latter shows that three Earth 2022, 3 718 technique mentioned earlier, as shown in Figures 15 and 16. The latter shows that three neighbourhoods of the city are located in the very high vulnerability region. These neigh- neighbourhoods of the city are located in the very high vulnerability region. These neigh- bourhoods have more than 23,000 residents, i.e., 3% of the city’s total population. In addi- bourhoods have more than 23,000 residents, i.e., 3% of the city’s total population. In addi- tion, only seven neighbourhoods with more than 36,000 residents, i.e., 5% of the total pop- tion, only seven neighbourhoods with more than 36,000 residents, i.e., 5% of the total pop- neighbourhoods of the city are located in the very high vulnerability region. These neigh- ulation, are located in the high vulnerability region. ulation, are located in the high vulnerability region. bourhoods have more than 23,000 residents, i.e., 3% of the city’s total population. In addition, only seven neighbourhoods with more than 36,000 residents, i.e., 5% of the total population, are located in the high vulnerability region. (a) (b) (a) (b) Figure 15. Sub-criteria of the building domain: (a) informal settlement rate (C5) and (b) lack of infra- Figure 15. Sub-criteria of the building domain: (a) informal settlement rate (C5) and (b) lack of infra- Figure 15. Sub-criteria of the building domain: (a) informal settlement rate (C5) and (b) lack of structure rate (C6). structure rate (C6). infra-structure rate (C6). Figure 16. Building vulnerability map (Vb). Figure 16. Building vulnerability map (Vb). 4.4. Aggregated Vulnerability Map Figure 16. Building vulnerability map (Vb). The aggregated vulnerability map (Va) was produced by multiplying Vu, VS and Vb using the FOG technique based on Formula (14). As shown in Figure 17, six neighbour- hoods of the city are located in the very high vulnerability region. These neighbourhoods have more than 106,000 residents, or 15% of the total population. In addition, 17 neigh- bourhoods with more than 204,000 people, or 29% of the total population, live in the high vulnerability area. Table 7 provides the spatial distribution of the population based on the Earth 2022, 3, 21 4.4. Aggregated Vulnerability Map The aggregated vulnerability map (Va) was produced by multiplying Vu, VS and Vb using the FOG technique based on Formula (14). As shown in Figure 17, six neighbour- hoods of the city are located in the very high vulnerability region. These neighbourhoods have more than 106,000 residents, or 15% of the total population. In addition, 17 neigh- Earth 2022, 3 719 bourhoods with more than 204,000 people, or 29% of the total population, live in the high vulnerability area. Table 7 provides the spatial distribution of the population based on the proposed vulnerability indicators , while Table A1 provides the codes and names of the neighbourho proposed ods of Nasiriyah C vulnerability indicators, ity in south Iraq. while Table A1 provides the codes and names of the neighbourhoods of Nasiriyah City in south Iraq. Figure 17. Classification of city neighbourhoods based on the aggregated vulnerability indicators (Va). Table 7. Classification of city neighbourhoods based on the aggregated vulnerability indicators. Figure 17. Classification of city neighbourhoods based on the aggregated vulnerability indicators (Va). Vulnerability Number of Neighbourhood Ratio from the Population Area (Hectares) Indicators Neighbourhoods Code Total Population Table 7. Classification of city neighbourhoods based on the aggregated vulnerability indicators. 38, 53, 56, 57, 62 Very high 6 106,809 15% 267 and 79 Vulnerability Number of Ratio from the Total Neighbourhood Code Population Area (Hectares) High 17 204,762 29% 557 Indicators Neighbourhoods Population Very high 6 38, 53, 56, 57, 62 and 79 106,809 15% 267 Medium 14 119,185 17% 433 High 17 204,762 29% 557 Low 11 77,596 11% 329 Medium 14 119,185 17% 433 Very low 13 61,194 9% 366 Low 11 77,596 11% 329 Very low 13 61,194 9% 366 4.5. Environmental Vulnerability Map (Ve) Overlapping of sub-criteria, i.e., high sources of pollution (C1), moderate sources of pollution (C2), low sources of pollution (C3) and effects of weapons and wars, DU landfill (C4), produced the environmental vulnerability map based on Formula (14), as shown in Figures 18–20. The environmental vulnerability map classifies the study area into five classes, as mentioned earlier. For higher accuracy, the values of pixels were extracted from the boundaries of each neighbourhood based on its coordinates. Figure 22 shows that six neighbourhoods of the city are located in the very high vulnerability area. More than 68,660 people, i.e., 9.7% of the total population of the study area, are living in these neigh- bourhoods, which are exposed to environmental risks. In addition, eight neighbourhoods with more than 38,000 residents, or 5.4% of the total population, are located in the high vulnerability region. Earth 2022, 3, 22 4.5. Environmental Vulnerability Map (Ve) Overlapping of sub-criteria, i.e., high sources of pollution (C1), moderate sources of pollution (C2), low sources of pollution (C3) and effects of weapons and wars, DU landfill (C4), produced the environmental vulnerability map based on Formula (14), as shown in Figures 18–20. The environmental vulnerability map classifies the study area into five classes, as mentioned earlier. For higher accuracy, the values of pixels were extracted from the boundaries of each neighbourhood based on its coordinates. Figure 21 shows that six neighbourhoods of the city are located in the very high vulnerability area. More than 68,660 people, i.e., 9.7% of the total population of the study area, are living in these neigh- bourhoods, which are exposed to environmental risks. In addition, eight neighbourhoods with more than 38,000 residents, or 5.4% of the total population, are located in the high Earth 2022, 3 720 vulnerability region. (a) (b) Earth 2022, 3, 23 Figure 18. Sub-criteria of the environmental domain: (a) map of the high sources of pollution (C1) Figure 18. Sub-criteria of the environmental domain: (a) map of the high sources of pollution (C1) and (b) map of the moderate sources of pollution (C2). and (b) map of the moderate sources of pollution (C2). (a) (b) Figure 19. Sub-criteria of the environmental domain: (a) map of the low-polluting projects (C3) and (b) buffer zones of DU landfill (C4). Figure 19. Sub-criteria of the environmental domain: (a) map of the low-polluting projects (C3) and (b) buffer zones of DU landfill (C4). Earth 2022, 3, 24 Earth 2022, 3 721 Figure 20. Environmental vulnerability map (Ve). 4.6. Comprehensive Vulnerability Map Figure 20. Environmental vulnerability map (Ve). By using the FOG function based on Formula (14), a first scenario of the comprehen- sive vulnerability map (Vt) was produced by multiplying Va by Ve. As shown in Figure 22, 11 city neighbourhoods are located in the very high vulnerability region. They are home to more than 175,000 residents, or 25% of the total population of the study area. Furthermore, 12 neighbourhoods with more than 115,000 residents, or 16% of the total population, are located in the high vulnerability region, as indicated in Table 8. The second scenario results showed that only five neighbourhoods with 104,000 residents, i.e., 15% of the total popula- tion, are located in the very high vulnerability region. Furthermore, 15 neighbourhoods Earth 2022, 3 722 with more than 202,208 persons, or 29% of the total population, are located in the high vulnerability region, as shown in Figure 21. Table 9 provides the second scenario results. Table 8. First classification scenario of city neighbourhoods based on the comprehensive vulnerability index (Vt). Vulnerability Number of Neighbourhood Ratio from the Population Area (Hectares) Indicators Neighbourhoods Code Total Population 6, 38, 53, 54, 55, 56, Very high 11 175,678 25% 431 57, 61, 76, 79, 85 High 12 115,841 16% 336 Medium 14 145,345 21% 503 Earth 2022, 3, 27 Low 13 93,033 13% 388 Very low 11 39,649 6% 293 Figure 22. Figure Second clas 21. Second sificati classification on scenario of scenario city neig of city hbou neighbour rhoods ba hoods sed on based the com on the p compr rehensive ehensive vul-vulner- nerability index (Vt). ability index (Vt). 4.7. Validation 4.7.1. Using Machine Learning (ML) The ML technique was used to verify the accuracy and robustness of the vulnerability map classification. ML is a technique that uses a small part of the data (the testing dataset) to evaluate a large part of the same dataset (a trained sample) [87]. The naïve Bayes (NB) classifier that is available in the Weka software was applied on the basis of Formula (17) [88]. Weka software is a group of ML algorithms for mining data; it is an open-source application [89]. The validation result showed that correctly classified instances were 90.4762%, and the kappa statistic value was 0.8786. Thus, the level of agreement of the classification results was demonstrated to be ‘almost perfect’. If the kappa value is be- tween 0.80 and 1, then the result is interpreted as an ‘almost perfect agreement’ [90]. ( ) 𝑃 𝑃 𝐴 (18) 𝑃 = 𝐵 𝑃(𝐵) P(A/B) presents the posterior likelihood, where (A) is the probability of the hypothe- sis, and (B) presents the observed event. P(B/A) represents probability: the probability of the proof given that the probability of a hypothesis is correct. Earth 2022, 3 723 Table 9. Second classification scenario of city neighborhoods based on the comprehensive vulnerabil- ity index (Vt). Vulnerability Number of Neighbourhood Ratio from the Population Area (Hectares) Indictors Neighbourhoods Code Total Population Very high 5 38, 53, 54, 56, 61 104,844 15% 255 High 15 202,208 29% 509 Medium 15 120,049 17% 484 Earth 2022, 3, Low 30 107,780 15% 430 26 Very low 11 34,665 5% 302 Figure 21. Figure First cla 22. ssif First icat classification ion scenario of scenario city neig of city hbourhoo neighbour ds ba hoods sed on th based e comprehensi on the compr vehensive e vulner- vulnera- ability index (Vt). bility index (Vt). Table 9. Second classification scenario of city neighborhoods based on the comprehensive vulnera- 4.7. Validation bility index (Vt). 4.7.1. Using Machine Learning (ML) The ML technique was used to verify the accuracy and robustness of the vulnerability Vulnerability Number of Ratio from the Total Neighbourhood Code Population Area (Hectares) map classification. ML is a technique that uses a small part of the data (the testing dataset) Indictors Neighbourhoods Population to evaluate a large part of the same dataset (a trained sample) [87]. The naïve Bayes (NB) Very high 5 38, 53, 54, 56, 61 104,844 15% 255 classifier that is available in the Weka software was applied on the basis of Formula (17) [88]. High 15 202,208 29% 509 Medium 15 120,049 17% 484 Low 30 107,780 15% 430 Very low 11 34,665 5% 302 Earth 2022, 3 724 Weka software is a group of ML algorithms for mining data; it is an open-source applica- tion [89]. The validation result showed that correctly classified instances were 90.4762%, and the kappa statistic value was 0.8786. Thus, the level of agreement of the classification results was demonstrated to be ‘almost perfect’. If the kappa value is between 0.80 and 1, then the result is interpreted as an ‘almost perfect agreement’ [90]. P P(A) A A P = (17) B P(B) P(A/B) presents the posterior likelihood, where (A) is the probability of the hypothesis, Earth 2022, 3, 28 and (B) presents the observed event. P(B/A) represents probability: the probability of the proof given that the probability of a hypothesis is correct. 4.7.2. Spatial Analysis Validation 4.7.2. Spatial Analysis Validation Three aerial photos of the study area were acquired, i.e., drone imagery (2009), Plei- Three aerial photos of the study area were acquired, i.e., drone imagery (2009), ades 1 ORTHO (2014) and Sentinel 2 imagery (October 2021), to validate the results of the Pleiades 1 ORTHO (2014) and Sentinel 2 imagery (October 2021), to validate the results comprehensive vulnerability map (Vt), as shown in Figure 21. Six neighbourhoods from of the comprehensive vulnerability map (Vt), as shown in Figure 22. Six neighbourhoods the eleven located in the very high vulnerability region (61, 54, 55, 56, 53 and 57) were from the eleven located in the very high vulnerability region (61, 54, 55, 56, 53 and 57) were spatially analysed as a sample to validate the vulnerability indicator results. Figure 23 spatially analysed as a sample to validate the vulnerability indicator results. Figure 23 shows the neighbourhoods located in the buffer zone of the main WWTP of the city, a shows the neighbourhoods located in the buffer zone of the main WWTP of the city, a high pollution source. In addition, the streets of this area are dusty (unpaved streets), and high pollution source. In addition, the streets of this area are dusty (unpaved streets), more than half of the total dwellings are informal housing. Compared with the other and more than half of the total dwellings are informal housing. Compared with the other neighbourhoods, these are the most vulnerable areas. The spatial analysis confirmed the neighbourhoods, these are the most vulnerable areas. The spatial analysis confirmed the validity of the results. validity of the results. Figure 23. Spatial analysis process for the region with very high vulnerability. Figure 23. Spatial analysis process for the region with very high vulnerability. Earth 2022, 3, 29 Earth 2022, 3 725 4.7.3. Sensitivity Analysis 4.7.3. Sensitivity Analysis Sensitivity analysis was used to investigate the model sensitivity to different criterion Sensitivity analysis was used to investigate the model sensitivity to different criterion weights. It is typically applied as a mechanism for assessing the responses of a model to weights. It is typically applied as a mechanism for assessing the responses of a model to modifying the input parameters and evaluating the reliability of the obtained results [91]. modifying the input parameters and evaluating the reliability of the obtained results [91]. Thus, model outcomes are substantial if the study results are altered when the input Thus, model outcomes are substantial if the study results are altered when the input weights of the criteria are different [92–94]. In this study, a sensitivity analysis process weights of the criteria are different [92–94]. In this study, a sensitivity analysis process was was applied to demonstrate the effect of different weights on the classification outcomes applied to demonstrate the effect of different weights on the classification outcomes to to verify the robustness or sensitivity of the proposed model versus the relative im- verify the robustness or sensitivity of the proposed model versus the relative importance portance of the major criteria. In addition, sensitivity analysis addresses the hypothesis of the major criteria. In addition, sensitivity analysis addresses the hypothesis that the that the study results will be changed if another scenario is used. In this context, another study results will be changed if another scenario is used. In this context, another scenario scenario was prepared in which the weights of the major criteria were changed. The clas- was prepared in which the weights of the major criteria were changed. The classes of the ses of the city’s neighbourhoods were changed when the new weighted values were in- city’s neighbourhoods were changed when the new weighted values were inputted, as putted, as illustrated in Figure 24. Consequently, the sensitivity analysis process con- illustrated in Figure 24. Consequently, the sensitivity analysis process confirmed that the firmed that the model results were robust. model results were robust. (a) (b) Figure 24. Change in a neighbourhood’s classification between the first scenario (a) and the second Figure 24. Change in a neighbourhood’s classification between the first scenario (a) and the second scenario (b). scenario (b). 5. Discussion Previous studies have used different methods to define vulnerable urban areas, and have adopted various criteria that are relevant to vulnerability assessment. Some scholars, such as Hazell (2020), classified the criteria into three categories (topographic, land cover attribute and demographic), whilst others, such as Gerundo, Marra and de Salvatore (2020), classified criteria into social, urban and building domains [7,28]. Similarly, Ruá et al. (2021) classified criteria into four categories: socioeconomic, sociodemographic, urban and building [30]. However, most of these approaches have disregarded potential environmental issues in urban areas resulting from urbanisation and human activities. Instead, they have focused primarily on financial and social criteria for studying land use Earth 2022, 3 726 change. Furthermore, the simulation results obtained were difficult to utilise in optimising land use on Earth [33]. In contrast with conventional approaches, the current study involved a new approach that is capable of comprehensively measuring vulnerability indicators (Vt), including environmental vulnerability indicators. Compared with previous studies, in which classical theory based on the logic of crisp sets was used, in the current study, FL was used to consider the uncertainty in the classification and combination of vulnerability indicators. In addition, the FOG function was applied to produce the final fuzzy map to balance the rising effect of the fuzzy sum and the lessening effect of the fuzzy product to obtain the best result. In contrast to previous studies that used various criteria derived only from the existing literature, the current study involved a conservative approach to confirm the relevance of the criteria with the actual reality of vulnerable urban areas. The proposed approach included three sequential phases, starting with selecting relevant criteria from the literature review, then using the Delphi technique to arrive at the group’s opinion to endorse these criteria, and relating the endorsed criteria to national urban and environmental indicators. Furthermore, this study used the JNB method to provide a more meaningful visualisation for the vulnerability maps. ML was used to validate the model results. Two different scenarios of the overall vulnerability maps were created to reduce evaluation subjectivity. The results indicated, both visually and statistically, that the city neighbourhoods suffered from environmental pollution and regional marginalisation. A large area of the city was suffering from pollution effects, with residential land use overlapping with polluted industrial use because of rapid urbanisation and poor land use. In addition, the comprehensive vulnerability maps showed that many neighbourhoods were located in very high and high vulnerability regions. The western part of the city, which is involved in future city expansion based on the master plan approved by local authorities, is located in a region with environmental pollution. By contrast, the northern part of the study area is outside the region with environmental pollution, and thus is suitable for future city extension. The conclusion can be drawn that local urban planning standards and environmental legislation have been disregarded in the planning stages for urban development. With respect to the comparability of Nasiriyah City, i.e., the case study, and other Arab cities, the study produced results that were consistent, to a certain extent, with those of other studies that have been conducted to define vulnerable urban areas in Egyptian cities. For example, a study conducted by Effat, Ramadan and Ramadan (2021) in Assiut City, Egypt, revealed that the informal settlement rate, population density, urban growth rate and the lack of essential services are the most significant factors that increase the degree of vulnerability in urban areas [95]. Similarly, another study conducted by Waly, Ayad and Saadallah (2021) in Alexandria City, Egypt indicated that demographic charac- teristics, infrastructure indicators, urban domain, unemployment and poverty were the most consequential factors leading to urban vulnerability in the city [96]. However, the current study utilized a new approach that can evaluate urban areas more realistically by adopting comprehensive vulnerability indicators, including environmental indicators that are integrated with social, urban and building indicators. The proposed comprehensive assessment approach can be more reliable as a decision support system for analysing urban areas, and for allocating required financial resources and efficiently executing mitigation processes for the most vulnerable Arabic urban areas and developing countries. In summary, compared with previous techniques, the proposed approach, based on vulnerability theory, contributes to identifying priority areas of intervention, exhibits novelty and makes a significant contribution to Earth’s sustainability. The proposed integration, i.e., using aggregated vulnerability indicators coupled with environment vulnerability indicators, enables the building of a robust database and provides a guide for comprehensive vulnerability assessment, offering an improved decision support system to determine priority areas for intervention in complex urban areas. In addition, this system Earth 2022, 3 727 can help optimise public spending to mitigate vulnerability as local authorities responsible for city services frequently have insufficient financial resources. Why is the identification of vulnerable urban areas necessary before starting with intervention procedures? Evaluating a city’s situation before implementing intervention procedures has many purposes: 1—to identify the magnitude of the problem and clarify why a comprehensive plan for mitigating city problems with four dimensions (urban, social, building and environment) is urgently needed; 2—to define intervention priorities based on accurate vulnerability indicators; and, 3—to prepare a spatial database for monitoring vulnerability indicators when implementing intervention plans to mitigate the adverse effects of urbanisation and human activities. 6. Conclusions Although SEA was introduced in the 1990s as an effective mechanism for assessing the environmental effect of polluting activities to preserve Earth’s sustainability, most previous studies have overlooked environmental pollution when defining vulnerable ur- ban areas. The current research attempts to bridge the research gap by presenting a new comprehensive assessment model for defining vulnerable urban areas based on vulnerabil- ity theory with four dimensions: urban, social, building and environment. To overcome uncertainty in expert opinions and uncertainty in the classification and combination of vulnerability indicators, three FL techniques were integrated, FAHP, FLM and FOG, to ensure a comprehensive assessment of vulnerability. The proposed approach adopted twelve criteria organised into four domains (building, social, urban and environment) to define a vulnerable urban area. Furthermore, the proposed vulnerability indicators were classified using the JNB classification technique, and then the results were validated via ML. The validation model that used ML confirmed that the level of agreement of the classification results was ‘almost perfect’. Therefore, the model can facilitate making a wise decision, particularly when city districts are suffering simultaneously from diverse adverse effects. The major contribution of this study is the provision of a powerful decision support system for the assessment and analysis of urban areas that are exposed to environmental degradation and spatial marginalisation. This system can be used to allocate the required financial resources and ensure mitigation processes are executed efficiently for the most vulnerable urban areas in Iraq and other developing countries. However, strict restrictions are imposed on accessing data regarding environmental pollution and social vulnerability at the household scale to analyse the effects of polluting projects on human health in very high vulnerability regions. In addition, no actual investigations have determined the effect of DU in the study area due to a lack of experience and tools. Nevertheless, international reports have indicated an increase in disease cases associated with DU in the study area. Thus, further studies that focus on the effects of DU in conflict areas in general, and in Iraq in particular, are urgently needed. Improving the ability to evaluate overall vulnerability in urban areas under rapid urbanisation and high population growth will be essential when formulating policies for urban communities and building sustainable livelihoods in developing countries. Author Contributions: Conceptualization, S.K.H. and A.F.A.; methodology, S.K.H.; software, S.K.H.; validation, S.K.H., A.F.A., H.Z.M.S. and A.W.; formal analysis, S.K.H.; investigation, S.K.H. and A.F.A.; resources, S.K.H.; data curation, S.K.H.; writing—original draft preparation, S.K.H.; writing—review and editing, S.K.H. and A.F.A.; supervision, S.K.H., A.F.A., H.Z.M.S. and A.W. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: All data are reported in this paper. Conflicts of Interest: The authors declare no conflict of interest. Earth 2022, 3 728 Appendix A Table A1. The codes and names of the neighbourhoods of Nasiriyah City in south Iraq. Neighbourhood Neighbourhood Name Name Code Code 1 Aljamaa 46 Alaskary_3 2 Sawage 47 Alhasan 3 Alseray 48 Bashaeer 4 Syaf 49 Rasool_1 5 Sabeah 50 Rasool_2-3 6 Alsharqyah_1 53 Feda_2 7 Alsharqyah_2 54 Alamen dakhaly_1 8 AbuJada_1 55 Alamen dakhaly_2 9 AbuJada_2 56 Alamen dakhaly_3 10 Alarooba 57 Karama_1 11 AladaraAlmahalyah 58 Karama_2 12 Alsalhyah_1 59 Tadahayh_1 13 Alsalhyah_2 60 Tadahayh_2 14 Alsalhyah_3 61 Tadahayh_3 15 Shuhada_1 62 Zahra 16 Shuhada_2 63 Beqaa 17 Shuhada_3 64 Khadrah 18 Shuhada_4 65 old askan_1 19 Shuhada_5 67 Old askan_3 20 Rafedeen 68 old askan_4 21 Arido_1 69 Mutanazah 22 Arido_2 70 Zauyah_Bs 23 Arido_3 71 Alaarja 24 Arido_4 72 Mansuryah_1 25 Arido_5 73 Mansuryah_2 26 Ind_n_1 74 Mansuryah_3 27 Ind_n_2 75 Thura_1 28 Sader_1 76 Thura_2 29 Sader_2 77 Thura_3 30 Sader_3 78 Zaaylat 31 Sader_4 79 Zaaylat_2 32 Ur_1 80 Zaaylat_3 33 Ur_2 81 Samood_fayth 34 Ur_3 82 Samood_2 35 Ur_4 84 Shaalah 36 Sumer_1 85 Sakak 37 Sumer_2 86 Alaskan_Sanay 38 Sumer_3 87 Alhbush 39 Sumer_4 88 Alamarat 40 Almulmeen_1 89 Shmukh 41 Almulmeen_2 90 Kanzawy 42 Almulmeen_3 91 Sader ccomplex 43 Almulmeen_4 92 University complex 44 Alaskary_1 144 Khatra-2 45 Alaskary_2 References 1. 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Journal

EarthMultidisciplinary Digital Publishing Institute

Published: Jun 8, 2022

Keywords: vulnerability; urban; environment; infrastructure; uranium; MCDM; fuzzy; GIS

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