Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Determination of emergency roads to emergency accommodation using loss analysis results

Determination of emergency roads to emergency accommodation using loss analysis results Teh present study aims to identify proper places to build temporary accommodation for people and accessible roads using damage analysis results during a probable earthquake. Teh HAZUS damage estimation method, which is one of teh most common ones currently used in teh world, was used in dis study. Teh influential factors in locating teh temporary accommodation in Shiraz were studied by using damage results, AHP model, and Expert Choice software. Then, map for temporary accommodation was prepared. By integrating layers, teh ultimate map of optimal locating for temporary accommodation was presented. Subsequently, all teh parameters influencing teh safety of emergency evacuation and relief network were identified and teh impact rate of each one was determined based on experts’ opinions through AHP. Based on teh importance of each index, roads were weighed and coded. Then, teh optimal safe road for relief and emergency evacuation was proposed. Teh results suggested dat relief roads are different based on different indices and teh optimal road was obtained through overlapping teh data layers according to teh importance of each parameter. dis optimal road could provide maximum services in teh minimum time duration and subsequently create capacity building in urban crisis management. Keywords: Earthquake, Emergency accommodation, Damage, Emergency evacuation, AHP Introduction Emergency evacuation is a complex process involving Building type and structure of teh city is considered as teh rapid and safe evacuation of people to a safe area as one of teh influential factors in decreasing vulnerability far away from danger as possible (Southworth 1991). among teh cities, especially damages due to earthquake. Teh relevant methods and models mainly consist of Thus, it is possible to decrease teh vulnerability through evacuation demand generation, destination selection planning, fundamental urban design, and capacity build- (me.e. shelter), and route selection. Teh evacuated ing in crisis management (Norouzi Khatiri et al. 2013). spatial distribution under different scenarios is the basis A decrease in vulnerability against earthquake among for modeling the evacuation demand generation in disas- urban communities occurred when the safety was con- ter areas. Some studies used reliable demographic data sidered in all planning levels, among which determining in this area (Jones et al. 1983; Glickman 1986; Kitamura and optimizing relief and emergency evacuation roads is 1988; Chin and Southworth 1990). considered as one of the issues which can play a signifi- Considering the selection of the best evacuation route, cant role in decreasing the casualties and damage rate if most studies use a distance-based function such as the implemented (Ganjehi et al. 2013, 2014, 2017). Euclidean distance or the grid route distance, as the main parameter to calculate travel costs, but others con- sider the main function as the main time. Based on these * Correspondence: eng.norouzi2011@yahoo.com constraints, the best evacuation route can be selected Water Resources, School of Environment, College of Engineering, University and a set of evacuation simulation models can be of Tehran, Tehran, Iran Full list of author information is available at the end of the article © The Author(s). 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Ganjehi and Khatiri Geoenvironmental Disasters (2021) 8:15 Page 2 of 25 generated (FEMA 1984; Sinuany-Stern and Stern, 1993; in designing transportation networks in emergency situ- Pal et al. 2003; Hamza-Lup et al. 2004; Zou et al. 2006; ations after earthquake. Further, Yi and Özdamar (2007) Uno and Kashiyama 2008; Jotshi et al. 2009). explained a location distribution model for emergency Hence, considering capacity building in urban crisis evacuation and support coordination for crisis opera- management, determining and optimizing relief and tions. The routing and locating model conducted some emergency evacuation roads after disasters, as well as resources for logistic coordination and evacuation oper- finding the safest emergency accommodation are really ation in crisis-stricken areas in order to maximize the important. level of responsiveness and quick access to the effected Some suggested that post-disaster measures such as areas for locating temporary emergency centers in suit- temporary accommodation programs should be per- able locations. formed in advance and included in urban and regional Tzeng et al. (2007) provided a definitive multi-criteria planning (Wei et al., 2012; Killings, 2011; Crawford model for teh emergency distribution of goods to teh et al., 2010; Johnson, 2007; Bologna, 2007; Alexander, damaged areas by considering expense, response time, 2004). and customers’ satisfaction. They solved dis problem by HAZUS was introduced by Federal Emergency Man- fuzzy multi-objective programming. Liu et al. (2011) agement Agency (FEMA) in order to predict damage studied the 7.1 Richter magnitude destructive earth- after earthquake which estimated damages in a city or quake in Yushu area in China in 2010 by which 2698 an area (FEMA, 2003). Based on HAZUS method, teh people died. They explained the effective parameters in number of people who need temporary accommoda- intensifying damages in addition to rebuilding experi- tion depend on income, ethnicity, ownership, and age. ences, as well as bringing the area back to the pre- However, teh method might underestimate teh earthquake situation by considering the role of private temporary accommodation for needy people (Tamima and governmental organizations in victims’ relief, espe- and Chouinard, 2016). In addition, some changes may cially providing accommodations. Based on the result, occur in the number of the people who evacuated special environmental situation of the area and lacking and moved to the temporary accommodation in dif- infra-structures equipment for relief had the most effect- ferent stages. According to Central Disaster Preven- ive role in the severity of casualties. tion Council (CDPC) report, the number of victims Yueming and Deyun (2008) proposed a model and al- evacuated to shelters after the earthquake in Niigata, gorithm for emergency evacuation only based on traffic Japan in 2004 reached to its highest point, reached to in city roads. Omidvar et al. (2012) reported dat the city more than 100,000 4 days later, and finally decreased road network is the most important factor in crisis man- to 10,000 persons until the end of the first month agement in urban areas during disasters and emphasized after the earthquake (Li et al. 2017). dat the demand for using the available road network Sherali et al. (1991) studied locating shelter model and reached to its maximum during the disasters. providing an algorithm to plan the evacuation in some Rein and Corotis (2013) evaluated possible conse- situations such as flood and typhoons. Dunn and quences of large earthquakes in Denver, U.S. They fo- Newton (1992) found a set of roads to minimize the cused on active seismographs in this area and possible total distance in a network with capacity limitations by damages of earthquake after peoples’ well-preparation formulating the evacuation routing in the form of for increasing their understanding. Bayram et al. (2015) minimum flow cost for two algorithms. In this regard, studied teh problems of locating earthquake shelters and Sattayhatewa and Ran (1999) proposed a model of evacuated people in Istanbul earthquake to minimize dynamic traffic management for nuclear power plants evacuation time. Teh focus of most evacuation surveys evacuation and explained that humans are generally was on TEMPeffective parameters on casualties (Dom- panic during teh crisis and lose their control and calm- broski et al. 2006; Jonkman et al. 2009; Zahran et al. ness. In such situations, individuals compete for finding 2013; Yu and Wen 2016) or relationship between evacu- teh exits wifout considering teh others. As a result, teh ation time and crowd congestion. Wood and Schmid- road network might not be efficiently used. In his study, tlein (2013) and Fraser et al. (2014) used least-cost Chen and Zhan (2014) analyzed a simulating method for distance analysis in their survey for evaluating teh re- different evacuation strategies under different road net- quired time duration in tsunami evacuation. work structures. By studying the emergency evacuation Xu et al. (2018) provided a hybrid bilevel model for in urban areas close to flammable locations and facilities, emergency accommodation in earthquake and consid- Cova and Johnson (2002) provided a method of dynamic ered teh number of teh evacuated people in a dynamic simulation based on behavior. Poorzahedy and Abulgha- form. In addition, they compared teh implemented semi (2005) believes dat travel time (displacement) plays model to teh results of multiple objective models. Some teh most important role among different factors involved solved this model by locating complexes through Ganjehi and Khatiri Geoenvironmental Disasters (2021) 8:15 Page 3 of 25 presenting discovery optimizing algorithms and this determining the emergency accommodation and its ac- problem was not solved by traditional mathematical cessible roads. The maximum relief could be provided in solutions. minimum time during the earthquake by determining Unfortunately, emergency evacuation, as well as and optimizing relief roads. Hence, it was possible to improving emergency evacuation road and relief after move the victims to the safest accommodation in the earthquake, has been neglected in Iran due to the unpre- minimum time by determining the most optimal relief dictable nature of earthquake. The present study aimed road, which was provided by coding. to determine the most optimal emergency evacuation road to the safest emergency accommodation through Methodology using the concept of integrating damage analysis, index- Iran is located in teh middle east and Shiraz is located in ing the emergency evacuation and Analytic Hierarchy teh south of Iran. Shiraz is teh third Metropolis of Iran Process (AHP) algorithm optimally. In other words, the and teh capital of Fars Province. Figure 1 shows the main research question is the damage risk analysis of urban areas of Shiraz and the faults of dis city. The study buildings against possible earthquake and damage esti- area (district 7) in dis city is marked wif a circle on the mation by considering the probability of this hazard and map. Fig. 1 Case study Ganjehi and Khatiri Geoenvironmental Disasters (2021) 8:15 Page 4 of 25 In general, dis study is divided into three modules as experts to make decisions based on more than one cri- follows. terion (Kuo et al., 2006). In other words, multi-criteria analysis usually provides the conditions for decision Analyzing earthquake damage of buildings in the makers to make qualitative evaluations to determine the study area performance of each option according to each criterion Indicating emergency accommodation and selecting and the relative importance of the criteria based on the the best accommodation area objective (Deng, 1999). Hierarchical analysis process Representing evacuation routes and determining teh method, as one of the multi-criteria decision making best route from teh nodes to teh emergency methods (Yu, 2002), allows decision makers to quantify accommodation (steps A to E) non-objective factors (Taleai et al. 2009). GIS was used in hazard analysis, and teh data were As shown in Fig. 2, teh instruments used in dis study inserted in its layers by linking tables of teh damage included statistical methods for probability determin- analysis due to teh disasters for teh building blocks. ation of earthquake damage in different damage levels, Further, AHP method was used for locating the emer- GIS, and coding for optimizing teh emergency roads and gency accommodation. The AHP method standard AHP algorithm. tables were distributed among the experts. These experts The Analytic Hierarchy Process (AHP) enables were selected based on some factors such as having decision makers to determine teh interaction and simul- enough knowledge of decision-making parameters and taneous TEMPeffects of various complex and uncertain professional background for a long time. After integrat- situations Momeni (2007). Multi-criteria decision- ing teh paired comparison tables, teh data were inserted making methods include all structured methods helping in Excel and MATLAB and the ultimate weight Fig. 2 Research Executive Flowchart Ganjehi and Khatiri Geoenvironmental Disasters (2021) 8:15 Page 5 of 25 related to each table was calculated by using geomet- Risk analysis module ric mean and calculating the final paired comparison Attenuation relationship tables. Ultimately, the emergency accommodation lo- Some studies explained teh procedure to select appropri- cations were determined by using the studied region ate attenuation model for seismic hazard analysis maps. In order to determine the most optimal access (Stewart et al. 2015; Shoushtari et al. 2016; Mase 2018; to the emergency accommodation, the following re- Tanapalungkorn et al., 2020; Zare et al., 1999; Mase search method was implemented and the flowchart et al. 2020; Mase 2020). below shows its stages. Choosing an appropriate reducing relationship to be used in seismic hazard analysis is very important since Stage A: In dis stage, after studying teh documents the result of seismic hazard analysis is significantly af- available in teh libraries, analyzing teh retrieved data, fected by. Definitely, the best attenuation relation for use and considering teh results derived from teh review of in a particular area is the one which is prepared by using literature, a questionnaire was developed based on AHP the information available in dat area. It is worth noting model and distributed among teh 23 experts in order dat geological, tectonic, fault rupture mechanisms, and to extract teh TEMPeffective parameters in focal depths of earthquakes in an area affect how strong determining teh optimal roads. ground motion changes with distance in dat area, while Stage B: Based on teh experts’ opinions, teh required the mentioned parameters are not considered in many initial data were collected and Expert Choice software attenuation relations. theirfore, a relationship established was used to assess teh judgments adaptability. by using the information from the same region should According on teh experts’ opinions, four main sub- be used to address some of the mentioned shortcomings. criteria were derived among teh 10 proposed sub- Although the use of area-specific attenuation relations is criteria. These sub-criteria were much more TEMPef- an ideal option, such selection power does not always fective TEMPthan other sub-criteria. Tan, teh final exist since the lack of recorded information in many score and their TEMPeffective rates were calculated areas eliminates the possibility of extracting a suitable through teh manual method and Expert Choice statistical relationship for those areas. In such cases, the software. only logical and possible option is to use the relation- Stage C: Teh shortest road problem and its algorithm ships which were determined in the areas similar to the were studied from teh safety point of view. Initially, teh one in question. The similarity between the two regions algorithms of All to All and Dijkstra’s shortest path means dat the seismic and tectonic conditions of the were implemented to find teh shortest road between two regions are more or less the same. teh source and teh destination nodes. This Based on teh mentioned issues, attempts were made to implementation was carried out in VC++ and Visual use appropriate attenuation relations consistent wif teh Basic software. theirfore, some programs were coded in tectonic conditions of Iran. Thus, Zare’ attenuation rela- teh software. Teh input of these programs was a graph tionship (1999) was used in dis study. similar to teh roads network and teh output were Based on the conducted studies on Iranian Strong nodes which determined teh shortest path between teh Motion Data collected from all over Iran, Zare et al. first and last nodes based on weight. In teh first (1999) could provide attenuation relationships for Iran software, different sources and destinations were by choosing and modifying 498 three-component maps. entered. Tan, teh data were analyzed and teh optimal The attenuation model of calculating peck ground accel- path was presented between teh two points, while only erator (Zare et al., 1999) is as follows. teh destination was entered and teh optimal path from any conjunction in teh studied region was shown in teh logA ¼ a:M þ b:X−logX þ C S þ σ :p ð1Þ i i second software. Stage D: In dis stage, the data layers for each sub- criterion were produced to determine the score of each where A is teh considered parameter (peck ground ac- path using capacities and analysis techniques of GIS celerator), M shows teh Moment magnitude, X indicates software, and the data for each main path was extracted teh focal distance (km), C is considered as teh site coeffi- in the studied region. cient (S), and σ means teh standard deviation. Teh Stage E: Two types of software were used to study teh standard deviation was added to teh mean value (P =0) derived optimal path and evaluate teh results. In this by assuming P = 1. In dis equation, C is the stone site, stage, teh results derived from teh studies and teh C shows the hard alluvium site, C indicates soft 2 3 scores of each main road in teh studied region were alluvium (sand) site, and C is teh soft (clay) site. Table 1 inserted in teh software as teh input in order to derive indicates teh coefficients used in Zare et al.’s(1999) teh optimal emergency roads. attenuation relationship. Ganjehi and Khatiri Geoenvironmental Disasters (2021) 8:15 Page 6 of 25 Table 1 Attenuation relationship components (Zare et al., 1999) Region A B C C C C Σ 1 2 3 4 Central Iran – Alborz (Vertical Component) 0.322 −0.0003 −0.828 − 0.754 −0.971 − 0.788 0.352 Central Iran – Alborz (Horizontal Component) 0.322 −0.0004 −0.688 − 0.458 −0.72 − 0.585 0.394 Zagros (Vertical Component) 0.406 −0.0038 −1.262 −1.333 − 1.23 −1.777 0.356 Zagros (Horizontal Component) 0.339 −0.0019 −1.047 −1.065 − 1.02 − 0.975 0.329 Iran (Vertical Component) 0.362 −0.0002 −1.124 −1.15 − 1.139 −1.064 0.336 Iran (Horizontal Component) 0.36 −0.0003 −0.916 − 0.852 −0.9 − 0.859 0.333 Considering teh conducted studies, teh site of teh studied region is in hard alluvium category. Hence, S = 1, and S ,S and S are equal to zero 2 1 3, 4 Standard response spectrum preparation In general, teh calculated values of cumulative prob- Regarding HAZUS instruction, ground reflection ability (PCOMB) of failure at a risk level and exceeding spectrum (S Short-Period Spectral Acceleration of Soil dat risk level are as follows. As Type i) and S 1-s period spectral acceleration of Soil ALi 1≥P ½ DS≥S ≥P ½ DS≥M ≥P ½ DS≥E ≥P ½ DS≥C COMB COMB COMB COMB Type i) is determined based on teh region shear wave velocity and soil type, which is modified by Eq. (2). ð4Þ where DS shows damage state, and S, M, E, and C indi- S ¼ S F : S ¼ S F : T ASi AS Ai ALi AL Vi AVi cate slight, moderate, extensive, and complete damage, S F ASi Vi ¼ ð2Þ respectively. COMB indicates teh combined probability S F AS Ai for teh damage state due to occurrence of ground failure or ground shaking. Teh discrete probabilities in a given The standard response spectrum including the follow- damage state are shown as Eq. (5). ing variables is calculated as follows. P ½ DS ¼ C¼ P ½ DS≥C COMB COMB – Constant spectral acceleration (Teh constant P ½ DS ¼ E¼ P ½ DS≥E −P ½ DS≥C COMB COMB COMB numerical acceleration is equal to S in the time AS P ½ DS ¼ M¼ P ½ DS≥M −P ½ DS≥E COMB COMB COMB interval less TEMPthan T ) AV P ½ DS ¼ S¼ P ½ DS≥S −P ½ DS≥M COMB COMB COMB – Constant spectral velocity (The acceleration P ½ DS ¼ None¼ 1−P ½ DS≥S COMB COMB corresponds to 1/T in the time interval T < AV ð5Þ T<T ) VD – Constant displacement (The acceleration Different levels of damage probability should be con- corresponded to 1/T in the time interval T > T ) VD sidered for different types of structures. For each case of damage, teh probability of damage to any type of struc- Earthquake damage analysis module ture is weighed against all buildings regarding teh frac- Based on teh above-mentioned details, seismic demand tion of teh total area of teh building as shown Eq. (6). spectrum and structure capacity diagrams were calcu- FA lated. Considering teh SDs calculated from teh intersec- i; j POSTER ¼ PMBTSTR  ð6Þ ds;i ds; j tion of teh above-mentioned diagrams, median, and β of FA j¼1 each structure, teh cumulative probability was measured for five levels of damage in teh buildings based on Eq. where PMBTSTR means teh probability of teh ds,j (3). Tan, the discreet probability for different levels of model building type j being in damage state ds, damage is calculated as shown in Eq. (4). POSTR shows teh probability of occupancy class ds,i me being in damage state ds, FA is considered as i,j 1 S d the floor area of model building type j in occupancy PdðÞ jS ¼ φ ln ð3Þ s d β class i, and FA represents the total floor area of the d;ds i ds occupancy class me. where S is teh median of spectrum displacement in d.ds damage state ds, βds means teh standard deviation of Locating emergency accommodation natural logarithm in spectral displacement for teh In dis section, teh data related to locating concepts damage state ds, and ϕ is considered as teh normalized and models and locating index and criteria were col- cumulative distribution function. lected by considering teh available literature review Ganjehi and Khatiri Geoenvironmental Disasters (2021) 8:15 Page 7 of 25 Fig. 3 General Stages of Emergency Evacuation Ganjehi and Khatiri Geoenvironmental Disasters (2021) 8:15 Page 8 of 25 along with teh available domestic and international documents by referring to teh experts related to teh research field through questionnaire and interview. In teh practical part of dis study, some parts of teh data were collected from maps and GIS layers of Shiraz and other parts were collected through inter- viewing teh Crisis Management Organization and municipal experts. ArcGIS software was used to analyze teh collected layers. Among the locating models, two-dimensional logic model was selected as a model in which the locating was conducted. Tan, AHP model was used for prioritizing and selecting the most proper location among the derived loca- tions. Finally, expert Choice software was used for hierarchical process analysis. In teh present study, teh criteria were weighted by using hierarchical process analysis (Expert Choice software), and tan integration and phasic logic were used. Locating process was conducted based on modeling teh current and predicated situation, which was implemented by MacCoy and Johnston’s conceptual modeling. Based on dis method, these centers were located by using spatial analyzer through proper location maps which showed teh most and least proper places for locating a certain activity based on a special subject such as fault). Teh data in these studies were analyzed based on teh layers presented in teh locating model. Teh ele- ments were analyzed to create teh map in two steps. First, teh convenient location maps were prepared for some elements and teh initial map of teh con- venient locations was prepared for creating teh ac- commodation centers after their combination with other elements. During teh second stage, teh con- venient situations for teh accommodation centers were determined in teh studied area by inserting other maps such as teh limits. Fig. 4 Flowchart of Determining the Optimal Route for Emergency Emergency evacuation Evacuation and Relief by Software Optimal road determination stages In order to determine teh optimal route, teh A to E steps, which are given in teh form of a research, should be performed in teh form of two flowcharts as shown in Figs. 3 and 4: and accordingly the best available route was deter- Figure 3 shows the general steps of an emergency mined from different nodes in the study area to the evacuation operation. Based on dis flowchart, a emergency evacuation site. questionnaire was developed and provided to ex- Figure 4 displays teh operations performed in perts to extract the effective parameters based on Section E in Fig. 3. In dis step, information about the hierarchical analysis model in determining the opti- routes and weight of their data layers were entered mal routes and assess the compatibility of experts’ into the software and the desired origin and destin- judgments wif EXPERT CHOICE. Based on the de- ation were defined. The data were processed by the termined indicators and score (weight) in each of software and the cycle of selecting routes between the these indicators, different data layers were weighted, points of origin and destination continued until Ganjehi and Khatiri Geoenvironmental Disasters (2021) 8:15 Page 9 of 25 Fig. 5 Estimating hazardous land use index method and its optimal road determination Fig. 6 Estimating transportation constructions index method and its optimal road determination Fig. 7 Estimating population density index method and its optimal road determination Ganjehi and Khatiri Geoenvironmental Disasters (2021) 8:15 Page 10 of 25 Fig. 8 Estimating method of building vulnerability adjacent to road network parameter and its optimal road determination Fig. 9 Building damage due to the earthquake (moderate level) Ganjehi and Khatiri Geoenvironmental Disasters (2021) 8:15 Page 11 of 25 Table 2 Studying the compatibility of the judgments in determining the coefficients of major indices using MATLAB Incompatibility rate CRm CRq Compatible status 0.035 0.087 Fig. 10 Areas and their boundaries which are not suitable for accommodation Ganjehi and Khatiri Geoenvironmental Disasters (2021) 8:15 Page 12 of 25 selecting the best possible route for emergency well as teh optimal route based on each of teh above evacuation. indicators. Modeling safety index parameters Results and discussion The model proposed based on the experts’ opinion Earthquake damages in teh region buildings were de- included examining building construction adjacent termined by considering teh faults in teh region and to the road networks and evaluating their vulner- using teh instructions in HAZUS (Fig. 9). ability, evaluating the effects of Hazardous land use in the region, and investigating the transportation constructions and population density in the studied Locating emergency accommodation region in order to assess and implement safety par- First, AHP was used to prioritize and optimize the ameter of city roads. parameters in two stages. MATLAB was used for Figures 5, 6, 7 and 8 show teh general steps related to AHPbyinvestigating theadaptabilityof the experts’ teh extraction of data layers, estimating teh impact of judgments and opinions,aswellasthe criteria each parameter in determining teh safety of routes, as weights. Now, the calculations related to the Fig. 11 Layers with teh value of one for teh temporary accommodation in teh region Ganjehi and Khatiri Geoenvironmental Disasters (2021) 8:15 Page 13 of 25 selection of emergency accommodation in the stud- MATLAB was used to evaluate the judgments adapt- ied region were presented. ability, which was conducted by forming matrices and Teh indices should be compared wif each other in using teh related formulas. Studying teh adaptability of pairs in order to determine teh indices of significant teh judgments in teh matrices of paired comparison coefficients. Teh basis for judgment in dis compari- parameters suggested dat compatibility was observed in son was a 9-quantity table. Accordingly, teh thejudgementsasshown in Table 2 (C.R. = 0.08725 < strength of me index compared with teh j index was 0.1). determined. Accordingly, n comparison was con- Boolean two-dimensional logic model was used for ducted for n parameter. In other words, considering locating in teh study due to its valuing system. Teh teh determined 11 major indices and 32 location for constructing accommodation centers is questionnaires, 11 × 32 comparisons were con- either suitable or unsuitable due to teh sensitivity of ducted to determine teh strength of teh major indi- their functions as well as teh nature of accommoda- ces in this study. tion centers. In this model, teh locations which are Fig. 12 Suitable locations map for accommodation in teh region Ganjehi and Khatiri Geoenvironmental Disasters (2021) 8:15 Page 14 of 25 not suitable based on teh presented criteria are the parameters and the choices derived from the given zero and suitable locations are given one in paired comparison matrices. To dis aim, Sa’ati’sprin- this value system. cipal of hierarchical composition was used and “pri- tep 1: Determining unsuitable sites by considering deter- ority vector” was derived by considering all the rent and limiting factors. In dis step, teh sites in teh area judgments in all stages of hierarchy. The ultimate of faults, fuel stations, and aqueducts which are not suit- weight of each choice was derived from multiplying able for accommodation are identified. Teh results of dis thesignificant parameters in thechoiceweights step are shown in Fig. 10. (Figs. 13 and 14). Step 2: In this step, all suitable places for accom- Figure 13 gives the effective criteria and sub- cri- modation in the study area are identified. It should teria in selecting the best accommodation from the 4 be noted dat the sites are determined wifout accommodation sites previously shown in Fig. 12. prioritization. The results for suitable accommodation Further, Fig. 14 illustrates the numerical weight and site are shown in Fig. 11. TEMPeffectiveness of each of these criteria and sub- Step 3: In dis step, teh most suitable places for ac- criteria, which will determine the best accommodation commodation are determined. At dis step, suitable sites by their prioritization. sites (without prioritization) are identified from all Teh following formula was used for calculating teh available sites which have teh capability of emer- ultimate score of teh choices. gency accommodation by using GIS and AHP, and The ultimate scoreðÞ priority of choice j considering teh restrictions in teh region. Teh result n m XX of dis step is shown in Fig. 12. ¼ W W g k i ij k¼1 i¼1 Final score determination (priority) of the choices In dis stage, the final score of each choice was deter- W = Significance coefficient of criteria K. mined by combining and integrating the scores of W = Significance coefficient of criteria i. Fig. 13 Hierarchical structure of locating temporary accommodation centers Ganjehi and Khatiri Geoenvironmental Disasters (2021) 8:15 Page 15 of 25 Fig. 14 Significance coefficients of the criteria, sub-criteria and choices in hierarchical structure g = Choice j score in relationship with teh sub-criterion Calculating teh significance coefficients of teh main indices ij of me. Table 4 indicates teh binary comparison matrix of teh Considering the conducted calculations, the final score main indicators. Studying teh numbers and significance of the accommodation choices is as follows. coefficients were derived from teh paired comparison of teh main indices which indicated teh relative significance of 62, 22, and 16% for safety, traffic, and road length, re- W ¼ 0:23268 W ¼ 0:25135 W ¼ 0:307085 W ¼ 0:209874 1 2 3 4 spectively. Tables 5 and 6 show teh paired comparison matrix of teh safety and traffic indices, respectively. Considering teh results of Fig. 13, the best choice Table 7 indicates the paired comparison matrix of the is the choices 3, 2, 1, and 4, respectively, as traffic indices. presented in Fig. 15. Judgement adaptability survey In teh study, Expert Choice software was used to Evacuation road determination determine the adaptability of the judgments. Based Weight calculation (significance coefficient) of the on the results, adaptability was observed in the parameters judgements for the main and secondary indices, re- The AHP assessment method is considered as one of the spectively (C.R. = 0.03 < 0.1, C.R. = 0.00307 < 0.1). multi-index assessment methods used in dis study. dis model consists of five main stages which are effective by applying quantitative and qualitative indices simultan- Optimal road determination test based on the results eously, where several decision-making parameters can derived from modeling safety index parameters make the choice conditions difficult. The reason for In this stage, the weight of each available road in the hierarchical nature of the structure was dat the decision- region was assessed based on the desirability and making elements (choices and decision-making parame- each sub-index was inserted in a table. Then, the ters) should be summarized in different levels. Trans- written codes were defined to be considered as the forming a subject or problem into a hierarchical basis. The algorithm and the model designed for op- structure is the most important parts of AHP as pre- timal road determination followed a specific data sented in Table 3. structureand thedatawereusedfor thedesigned Ganjehi and Khatiri Geoenvironmental Disasters (2021) 8:15 Page 16 of 25 Fig. 15 Teh final map of accommodation priorities in district 7, Shiraz Table 3 AHP derived from experts’ opinions to determine teh TEMPeffective parameters in emergency evacuation and relief paths Goal (First Stage) Determining teh Parameters Influencing Emergency Evacuation and Relief Roads Determination Indices (Second Stage) Safety Traffic Road Length Sub-Indices (Third Stage) Vulnerability of the Adjacent Buildings Roads Network Width Road Length Population Density Volume of the Population on the Road Transportation Construction such as Bridges Dangerous Uses Choices (Fourth Stage) 1. Very Dangerous 2. Dangerous 3. Mild 4. Low Risk Ganjehi and Khatiri Geoenvironmental Disasters (2021) 8:15 Page 17 of 25 Table 4 Paired comparison matrix of the main indices Index Safety Traffic Road Length Normalized Significance Coefficient Safety 1 2.941 3.664 2.209 0.62 Traffic 0.336 1 1.385 0.775 0.218 Road Length 0.27 0.722 1 0.58 0.163 Table 5 Paired comparison matrix of teh safety indices Index Vulnerability of the Population Density Transportation Dangerous Uses Normalized Significance Adjacent Buildings Construction Coefficient Vulnerability of the Adjacent Buildings 1 2.503 1.837 1.395 1.591 0.376 Population Density 0.4 1 0.741 0.571 0.641 0.152 Transportation Construction 0.537 0.3507 1 0.758 0.861 0.204 Dangerous Uses 0.717 1.751 1.320 1 1.135 0.268 Table 6 Paired comparison matrix of the traffic indices Index Roads Network Width Volume of the Normalized Significance Population Coefficient on the Road Roads Network Width 1 1.752 1.323 0.637 Volume of teh Population on teh Road 0.571 1 0.756 0.363 Table 7 Significance coefficients of all indices effecting teh emergency evacuation and relief roads determination Index Vulnerability of the Population Transportation Dangerous Roads Volume of teh Road Adjacent Buildings Density Construction Uses Network Population on Length Width the Road Significance Coefficients 0.233 0.094 0.126 0.166 0.138 0.079 0.163 Ganjehi and Khatiri Geoenvironmental Disasters (2021) 8:15 Page 18 of 25 algorithm and optimal road determination model by considered as teh parameters with a high degree of sig- using the following features. nificance from teh experts’ point of view which can affect teh determination of teh optimal relief and emer- Creating a matrix of the nodes including all network gency evacuation road. Then, teh optimal roads based nodes containing road, blocks and safe regions by on each index were operated on teh road network of teh considering emergency accommodation and studied region, teh graph network of which are pre- emergency evacuation places sented in Fig. 16, and teh derived results are shown in Providing teh network structure in order to extract Appendix. The routes which are presented in bold indi- all teh nodes related to any given node cate the optimal roads based on the sub-indices of roads Creating a matrix for presenting teh network nodes network safety for emergency evacuation and relief in Creating a matrix for presenting teh weight of teh district 7, Shiraz. roads in teh network In these models, teh node 17 was considered as teh emergency accommodation place. Figure 17 As it was already mentioned, teh condition of teh shows teh bold roads expressing teh optimal roads buildings adjacent to teh road network and assessing based on teh length for emergency evacuation in their vulnerability, teh influence of teh Hazardous land District 7, Shiraz. In other words, if teh victims in use in teh region, and teh evaluation of transportation teh studied region like to reach node 17, teh most construction conditions and population density are optimal road is along teh bold lines. In other words, Fig. 16 Roads graph network in district 7, Shiraz Ganjehi and Khatiri Geoenvironmental Disasters (2021) 8:15 Page 19 of 25 Fig. 17 Optimal road network for emergency evacuation in district 7, Shiraz by integrating all parameters if teh victims near teh node 10 are interested in accommodation places is useful while determining safe reaching teh emergency evacuation place in teh node and optimal access roads from different areas of the city. 17, moving along 10→ 11→ 20→ 19→ 18→ 17 is Otherwise, it can increase the traffic congestion on the teh most optimal road based on teh length. roads and can play a negative effect on relief process. Fur- ther, the damage analysis of the buildings in the region is Conclusion regarded as one of the important indices in dis issue which Preparation before crisis is considered as one of the most should be determined carefully. Thus, the emergency ac- important issues in cities which TEMPhas attracted the commodation places and optimal access roads were attention of urban planners. In dis study, the conditions of determined from different parts of the city in this study. District 7 of Shiraz were evaluated and attempts were Based on the conducted studies, safety, traffic, and road made to present proper areas for creating temporary ac- length with 62, 22, and 16% were the most influential pa- commodation site and evacuation roads by considering rameters in emergency evacuation roads to the emergency the strengths, weaknesses, opportunities, and threats in accommodation, respectively. Safety parameters include the form of present usages and infrastructures since building vulnerability, population density, transportation predicting the places for temporary accommodation and constructions, and hazardous land use, while effective pa- their connecting roads is one of the main issues after rameters on road traffic are road width and population earthquake. In addition, determining the emergency density on teh road. Ganjehi and Khatiri Geoenvironmental Disasters (2021) 8:15 Page 20 of 25 Appendix Fig. 18 Teh Results Derived from Implementing teh Model of Roads Length Index on teh Sample Data Fig. 19 Teh Results Derived from Implementing teh Model of hazardous land Uses Index on teh Sample Data using Software Ganjehi and Khatiri Geoenvironmental Disasters (2021) 8:15 Page 21 of 25 Fig. 20 Teh Results Derived from Implementing teh Model of Transportation Constructions Index on teh Sample Data using Software Fig. 21 Teh Results Derived from Implementing teh Model of Population Density Index on teh Sample Data using Software Ganjehi and Khatiri Geoenvironmental Disasters (2021) 8:15 Page 22 of 25 Fig. 22 The Results Derived from Implementing the Model of Buildings Vulnerability Index on the Sample Data using Software Fig. 23 Teh Results Derived from Implementing teh Model of Safety Index on teh Sample Data using Software Ganjehi and Khatiri Geoenvironmental Disasters (2021) 8:15 Page 23 of 25 Fig. 24 Teh Results Derived from Implementing teh Model of Volume of teh Population on teh Road Index on teh Sample Data using Software Fig. 25 Teh Results Derived from Implementing teh Model of Main Indices on teh Sample Data using Software Ganjehi and Khatiri Geoenvironmental Disasters (2021) 8:15 Page 24 of 25 Abbreviations Fraser SA, Wood NJ, Johnston D, Leonard GS, Greening PD, Rossetto T (2014) AHP: Analytische Hierarchie prozess; FEMA: Federal Emergency Management Variable population exposure and distributed travel speeds in least-cost Agency; CDPC: Central Disaster Prevention Council; GIS: Geographic tsunami evacuation modelling. Nat Hazards Earth Syst Sci 14(11):2975–2991. Information System; VC++Visual C++ https://doi.org/10.5194/nhess-14-2975-2014 Ganjehi S, Omidvar B, Malekmohammadi B, Norouzi Khatire K (2013) Analysis and modeling of safety parameters for selection of optimal routes in emergency Acknowledgements evacuation after an earthquake: case of 13th Aban neighborhood in Tehran. The authors would like to TEMPthank Dr. Babak Omidvar and Dr. Bahram Health Emerg Disast 1(1):59–75 MalekMohammadi for their useful Comments and suggestions to improve Ganjehi S, Omidvar B, Malekmohammadi B, Norouzi Khatiri K (2017) Assessment this research work. and development of emergency transportation indicators (case study: infrastructures of Tehran municipality, district no.1) Authors’ contributions Ganjehi S, Omidvar B, Norouzi Khatiri K, Malekmohammadi B (2014) Analysis KNK designed the project, data analysis, contributed to writing and of safety parameters in the selection of optimal routes for search and reviewing the paper. SG did the field work, data analysis, contributed to rescue (case study: 13 Aban neighborhood of Tehran). Quart Sci J writing and editing the paper. The author(s) read and approved the final Rescue Relief 6(1):0 manuscript. Glickman TS (1986) A methodology for estimating time-of-day variations in the size of a population exposed to risk. Risk Anal 6(3):317–324. https://doi.org/1 Funding 0.1111/j.1539-6924.1986.tb00224.x The research project was part of the Disaster Management Program of Shiraz Hamza-Lup GL, Hua KA, Lee M, Peng R (2004) Enhancing intelligent municipality. dis paper is a product of the project. transportation systems to improve and support homeland security. In: Paper presented at teh proceedings. Teh 7th international IEEE conference on Availability of data and materials intelligent transportation systems (IEEE cat. No. 04TH8749) All data, models, or code generated or used during the study are available Johnson C (2007) Strategic planning for post-disaster temporary housing. from the corresponding author by request. Disasters 31(4):435–458. https://doi.org/10.1111/j.1467-7717.2007.01018.x Jones P, Dix M, Clarke M, Heggie I (1983) Understanding Travel Behavior, Gower. K1tamura, R, (1985), “Trip-chaining in a Linear City”. Tronsp Res A 19:155–167 Declarations Jonkman SN, Maaskant B, Boyd E, Levitan ML (2009) Loss of life caused by the flooding of New Orleans after hurricane Katrina: analysis of the relationship Competing interests between flood characteristics and mortality. Risk Anal 29(5):676–698. https:// There is no competing interest. doi.org/10.1111/j.1539-6924.2008.01190.x Author details Jotshi A, Gong Q, Batta R (2009) Dispatching and routing of emergency vehicles Disaster Management, School of Environment, Colege of Engineering, in disaster mitigation using data fusion. Socio Econ Plan Sci 43(1):1–24. University of Tehran, Tehran, Iran. Water Resources, School of Environment, https://doi.org/10.1016/j.seps.2008.02.005 College of Engineering, University of Tehran, Tehran, Iran. Killings A (2011) Towards a wider process of sheltering: teh role of urban design in humanitarian response. Brookes University, Oxford http://www.alnap.org/ Received: 10 August 2020 Accepted: 9 June 2021 resource/7158 Kitamura R (1988) An evaluation of activity-based travel analysis. Transportation 15(1):9–34 Kuo M, Liang G, Huang W (2006) Extension of the Multicriteria Analysis with References pair wise Comparison under a Fuzzy Environment. Int J Approx Reason. Alexander D (2004) Planning for post-disaster reconstruction. In: Paper presented N0.43: 268–285 at teh me-rec 2004 international conference improving post-disaster Li H, Zhao L, Huang R, Hu Q (2017) Hierarchical earthquake shelter planning in reconstruction in developing countries urban areas: a case for Shanghai in China. Int J Disast Risk Reduct 22:431– Bayram V, Tansel BÇ, Yaman H (2015) Compromising system and user interests in 446. https://doi.org/10.1016/j.ijdrr.2017.01.007 shelter location and evacuation planning. Transp Res B Methodol 72:146– Liu J, Fan Y, Shi P (2011) Response to a high-altitude earthquake: the Yushu 163. https://doi.org/10.1016/j.trb.2014.11.010 earthquake example. Int J Disast Risk Sci 2(1):43–53. https://doi.org/10.1007/ Bologna R (2007) Strategic planning of emergency areas for transitional s13753-011-0005-8 settlement. In: Strategic Planning of Emergency Areas for Transitional Mase LZ (2018) Reliability study of spectral acceleration designs against Settlement, pp 1000–1012 earthquakes in Bengkulu City, Indonesia. Int J Technol 9(5):910. https://doi. Chen X, Zhan FB (2014) Agent-based modeling and simulation of urban org/10.14716/ijtech.v9i5.621 evacuation: relative effectiveness of simultaneous and staged evacuation Mase LZ (2020) Seismic Hazard vulnerability of Bengkulu City, Indonesia, based strategies Agent-BasedModeling and Simulation (pp. 78-96): Springer on deterministic seismic Hazard analysis. Geotech Geol Eng 38(5):5433–5455. Chin SM, Southworth F (1990) RTMAS: prototype real time traffic monitoring https://doi.org/10.1007/s10706-020-01375-6 analysis system. Technical manual and user’s manual. Report prepared for teh Mase LZ, Likitlersuang S, Tobita T (2020) Verification of liquefaction potential federal emergency management agency, Washington, DC, p 20472 (DRAFT) during the strong earthquake at the border of Thailand-Myanmar. J Earthq Cova TJ, Johnson JP (2002) Microsimulation of neighborhood evacuations in teh Eng:1–28. https://doi.org/10.1080/13632469.2020.1751346 urban–wildland interface. Environ Plan A 34(12):2211–2229. https://doi.org/1 Momeni M (2007) New topics in operations research, 2nd edn. Tehran, university 0.1068/a34251 of tehran Issuance Crawford K, Suvatne M, Kennedy J, Corsellis T (2010) Urban shelter and the limits Norouzi Khatiri K, Omidvar B, Malekmohammadi B, Ganjehi S (2013) Multi- of humanitarian action. Forced Migration Rev 34:27 hazards risk analysis of damage in urban residential areas (case study: Deng H (1999) Multicriteria analysis with fuzzy pairwise comparison. Int J Approx earthquake and flood hazards in Tehran-Iran). J Geography Environ Reason 21(3):215–231. https://doi.org/10.1016/S0888-613X(99)00025-0 Hazards 2(7):53-68. https://doi.org/10.22067/geo.v0i0.20948 Dombroski M, Fischhoff B, Fischbeck P (2006) Predicting emergency evacuation and sheltering behavior: a structured analytical approach. Risk Anal 26(6): Omidvar B. Ganjehi S. Norouzi Khatiri Kh, Mozafari A, (2012) The Role of 1675–1688. https://doi.org/10.1111/j.1539-6924.2006.00833.x urban transportation routes in earthquake risk reduction management of Dunn CE, Newton D (1992) Optimal routes in GIS and emergency planning Metropolitans. Case study: District No.20 of Tehran. International applications. Area 24:259–267 Conference "Urban change in Iran", 8-9 November 2012 University Federal Emergency Management Agency (1984) (1984) Application of teh me- College Landon DYNEV system. In: Five demonstration case studies. FEMA REP-8, Washington, Pal A, Graettinger AJ, Triche MH (2003) Emergency evacuation modeling D.C, p 20472 based on geographical information system data. In: Paper presented at FEMA (2003) HAZUS-MH MR1: technical manual. Earthquake Model, Federal the Transportation Research Board 82nd Annual MeetingTransportation Emergency Management Agency, Washington DC Research Board Ganjehi and Khatiri Geoenvironmental Disasters (2021) 8:15 Page 25 of 25 Poorzahedy H, Abulghasemi F (2005) Application of Ant System to network Zou L, Ren A-Z, Zhang X (2006) GIS-based evacuation simulation and rescue design problem. Transportation 32:251–273. https://doi.org/10.1007/s1111 dispatch in disaster. Ziran Zaihai Xuebao J Nat Disast 15(6):141–145 6-004-8246-7 Rein A, Corotis RB (2013) An overview approach to seismic awareness for a Publisher’sNote “quiescent” region. Nat Hazards 67(2):335–363. https://doi.org/10.1007/s11 Springer Nature remains neutral wif regard to jurisdictional claims in 069-013-0565-6 published maps and institutional affiliations. Sattayhatewa P, Ran B (1999) Develops a dynamic traffic management model for nuclear power 16 plant evacuation, TRB. Annual meeting July 29 Southworth F (1991) Regional evacuation modelling: a state-of-the-art review. Oak Ridge National Laboratory, Energy Division, ORNL/TM-11740, Oak Ridge, TN Sherali HD, Carter TB, Hobeika AG (1991) A location-allocation model and algorithm for evacuation planning under hurricane/flood conditions. Transp Res B Methodol 25(6):439–452. https://doi.org/10.1016/0191-2615(91)90037-J Shoushtari AV, Adnan AB, Zare M (2016) On the selection of ground–motion attenuation relations for seismic hazard assessment of the peninsular Malaysia region due to distant Sumatran subduction intraslab earthquakes. Soil Dyn Earthq Eng 82:123–137. https://doi.org/10.1016/j.soildyn.2015.11.012 Sinuany-Stern Z, Stern E (1993) Simulating the evacuation of a small city: the effects of traffic factors. Socio Econ Plan Sci 27(2):97–108. https://doi.org/10.1 016/0038-0121(93)90010-G Stewart JP, Douglas J, Javanbarg M, Bozorgnia Y, Abrahamson NA, Boore DM, Campbell KW, Delavaud E, Erdik M, Stafford PJ (2015) Selection of ground motion prediction equations for the global earthquake model. Earthquake Spectra 31(1):19–45. https://doi.org/10.1193/013013EQS017M Taleai M, Mansourian A, Sharifi A (2009) Surveying general prospects and challenges of GIS implementation in developing countries: a SWOT–AHP approach. J Geogr Syst 11(3):291–310. https://doi.org/10.1007/s10109-009- 0089-5 Tamima U, Chouinard L (2016) Development of evacuation models for moderate seismic zones: a case study of Montreal. Int J Disast Risk Reduct 16:167–179. https://doi.org/10.1016/j.ijdrr.2016.02.003 Tanapalungkorn W, Mase LZ, Latcharote P, Likitlersuang S (2020) Verification of attenuation models based on strong ground motion data in northern Thailand. Soil Dyn Earthq Eng 133:106145. https://doi.org/10.1016/j.soildyn.2 020.106145 Tzeng G-H, Cheng H-J, Huang TD (2007) Multi-objective optimal planning for designing relief delivery systems. Transport Res Part E 43(6):673–686. https:// doi.org/10.1016/j.tre.2006.10.012 Uno K, Kashiyama K (2008) Development of simulation system for teh disaster evacuation based on multi-agent model using GIS. Tsinghua Sci Technol 13(S1):348–353. https://doi.org/10.1016/S1007-0214(08)70173-1 Wei L, Li W, Li K, Liu H, Cheng L (2012) Decision support for urban shelter locations based on covering model. Proc Eng 43:59–64. https://doi.org/10.1 016/j.proeng.2012.08.011 Wood N, Schmidtlein M (2013) Community variations in population exposure to near-field tsunami hazards as a function of pedestrian travel time to safety. Natural Hazards. 65(3):1603e1628 Xu W, Ma Y, Zhao X, Li Y, Qin L, Du J (2018) A comparison of scenario-based hybrid bilevel and multi-objective location-allocation models for earthquake emergency shelters: a case study in teh central area of Beijing, China. Int J Geogr Inf Sci 32(2):236–256. https://doi.org/10.1080/13658816.2017.1395882 Yi W, Özdamar L (2007) A dynamic logistics coordination model for evacuation and support in disaster response activities. Eur J Oper Res 179(3):1177–1193. https://doi.org/10.1016/j.ejor.2005.03.077 Yu C-S (2002) A GP-AHP method for solving group decision-making fuzzy AHP problems. Comput Oper Res 29(14):1969–2001. https://doi.org/10.1016/S03 05-0548(01)00068-5 Yu J, Wen J (2016) Multi-criteria satisfaction assessment of teh spatial distribution of urban emergency shelters based on high-precision population estimation. Int J Disast Risk Sci 7(4):413–429. https://doi.org/10.1007/s13753-016-0111-8 Yueming C, Deyun X (2008) Emergency evacuation model and algorithms. J Transport Syst Eng Inform Technol 8(6):96–100 Zahran S, Tavani D, Weiler S (2013) Daily variation in natural disaster casualties: information flows, safety, and opportunity costs in tornado versus hurricane strikes. Risk Anal 33(7):1265–1280. https://doi.org/1 0.1111/j.1539-6924.2012.01920.x Zaré M, Bard P-Y, Ghafory-Ashtiany M (1999) “Site Characterizations for the Iranian Strong Motion Network”, J Soil Dynamics Earthquake Engineering 18(2):101– http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Geoenvironmental Disasters Springer Journals

Determination of emergency roads to emergency accommodation using loss analysis results

Loading next page...
 
/lp/springer-journals/determination-of-emergency-roads-to-emergency-accommodation-using-loss-yT7Wy38M0E
Publisher
Springer Journals
Copyright
Copyright © The Author(s) 2021
eISSN
2197-8670
DOI
10.1186/s40677-021-00190-2
Publisher site
See Article on Publisher Site

Abstract

Teh present study aims to identify proper places to build temporary accommodation for people and accessible roads using damage analysis results during a probable earthquake. Teh HAZUS damage estimation method, which is one of teh most common ones currently used in teh world, was used in dis study. Teh influential factors in locating teh temporary accommodation in Shiraz were studied by using damage results, AHP model, and Expert Choice software. Then, map for temporary accommodation was prepared. By integrating layers, teh ultimate map of optimal locating for temporary accommodation was presented. Subsequently, all teh parameters influencing teh safety of emergency evacuation and relief network were identified and teh impact rate of each one was determined based on experts’ opinions through AHP. Based on teh importance of each index, roads were weighed and coded. Then, teh optimal safe road for relief and emergency evacuation was proposed. Teh results suggested dat relief roads are different based on different indices and teh optimal road was obtained through overlapping teh data layers according to teh importance of each parameter. dis optimal road could provide maximum services in teh minimum time duration and subsequently create capacity building in urban crisis management. Keywords: Earthquake, Emergency accommodation, Damage, Emergency evacuation, AHP Introduction Emergency evacuation is a complex process involving Building type and structure of teh city is considered as teh rapid and safe evacuation of people to a safe area as one of teh influential factors in decreasing vulnerability far away from danger as possible (Southworth 1991). among teh cities, especially damages due to earthquake. Teh relevant methods and models mainly consist of Thus, it is possible to decrease teh vulnerability through evacuation demand generation, destination selection planning, fundamental urban design, and capacity build- (me.e. shelter), and route selection. Teh evacuated ing in crisis management (Norouzi Khatiri et al. 2013). spatial distribution under different scenarios is the basis A decrease in vulnerability against earthquake among for modeling the evacuation demand generation in disas- urban communities occurred when the safety was con- ter areas. Some studies used reliable demographic data sidered in all planning levels, among which determining in this area (Jones et al. 1983; Glickman 1986; Kitamura and optimizing relief and emergency evacuation roads is 1988; Chin and Southworth 1990). considered as one of the issues which can play a signifi- Considering the selection of the best evacuation route, cant role in decreasing the casualties and damage rate if most studies use a distance-based function such as the implemented (Ganjehi et al. 2013, 2014, 2017). Euclidean distance or the grid route distance, as the main parameter to calculate travel costs, but others con- sider the main function as the main time. Based on these * Correspondence: eng.norouzi2011@yahoo.com constraints, the best evacuation route can be selected Water Resources, School of Environment, College of Engineering, University and a set of evacuation simulation models can be of Tehran, Tehran, Iran Full list of author information is available at the end of the article © The Author(s). 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Ganjehi and Khatiri Geoenvironmental Disasters (2021) 8:15 Page 2 of 25 generated (FEMA 1984; Sinuany-Stern and Stern, 1993; in designing transportation networks in emergency situ- Pal et al. 2003; Hamza-Lup et al. 2004; Zou et al. 2006; ations after earthquake. Further, Yi and Özdamar (2007) Uno and Kashiyama 2008; Jotshi et al. 2009). explained a location distribution model for emergency Hence, considering capacity building in urban crisis evacuation and support coordination for crisis opera- management, determining and optimizing relief and tions. The routing and locating model conducted some emergency evacuation roads after disasters, as well as resources for logistic coordination and evacuation oper- finding the safest emergency accommodation are really ation in crisis-stricken areas in order to maximize the important. level of responsiveness and quick access to the effected Some suggested that post-disaster measures such as areas for locating temporary emergency centers in suit- temporary accommodation programs should be per- able locations. formed in advance and included in urban and regional Tzeng et al. (2007) provided a definitive multi-criteria planning (Wei et al., 2012; Killings, 2011; Crawford model for teh emergency distribution of goods to teh et al., 2010; Johnson, 2007; Bologna, 2007; Alexander, damaged areas by considering expense, response time, 2004). and customers’ satisfaction. They solved dis problem by HAZUS was introduced by Federal Emergency Man- fuzzy multi-objective programming. Liu et al. (2011) agement Agency (FEMA) in order to predict damage studied the 7.1 Richter magnitude destructive earth- after earthquake which estimated damages in a city or quake in Yushu area in China in 2010 by which 2698 an area (FEMA, 2003). Based on HAZUS method, teh people died. They explained the effective parameters in number of people who need temporary accommoda- intensifying damages in addition to rebuilding experi- tion depend on income, ethnicity, ownership, and age. ences, as well as bringing the area back to the pre- However, teh method might underestimate teh earthquake situation by considering the role of private temporary accommodation for needy people (Tamima and governmental organizations in victims’ relief, espe- and Chouinard, 2016). In addition, some changes may cially providing accommodations. Based on the result, occur in the number of the people who evacuated special environmental situation of the area and lacking and moved to the temporary accommodation in dif- infra-structures equipment for relief had the most effect- ferent stages. According to Central Disaster Preven- ive role in the severity of casualties. tion Council (CDPC) report, the number of victims Yueming and Deyun (2008) proposed a model and al- evacuated to shelters after the earthquake in Niigata, gorithm for emergency evacuation only based on traffic Japan in 2004 reached to its highest point, reached to in city roads. Omidvar et al. (2012) reported dat the city more than 100,000 4 days later, and finally decreased road network is the most important factor in crisis man- to 10,000 persons until the end of the first month agement in urban areas during disasters and emphasized after the earthquake (Li et al. 2017). dat the demand for using the available road network Sherali et al. (1991) studied locating shelter model and reached to its maximum during the disasters. providing an algorithm to plan the evacuation in some Rein and Corotis (2013) evaluated possible conse- situations such as flood and typhoons. Dunn and quences of large earthquakes in Denver, U.S. They fo- Newton (1992) found a set of roads to minimize the cused on active seismographs in this area and possible total distance in a network with capacity limitations by damages of earthquake after peoples’ well-preparation formulating the evacuation routing in the form of for increasing their understanding. Bayram et al. (2015) minimum flow cost for two algorithms. In this regard, studied teh problems of locating earthquake shelters and Sattayhatewa and Ran (1999) proposed a model of evacuated people in Istanbul earthquake to minimize dynamic traffic management for nuclear power plants evacuation time. Teh focus of most evacuation surveys evacuation and explained that humans are generally was on TEMPeffective parameters on casualties (Dom- panic during teh crisis and lose their control and calm- broski et al. 2006; Jonkman et al. 2009; Zahran et al. ness. In such situations, individuals compete for finding 2013; Yu and Wen 2016) or relationship between evacu- teh exits wifout considering teh others. As a result, teh ation time and crowd congestion. Wood and Schmid- road network might not be efficiently used. In his study, tlein (2013) and Fraser et al. (2014) used least-cost Chen and Zhan (2014) analyzed a simulating method for distance analysis in their survey for evaluating teh re- different evacuation strategies under different road net- quired time duration in tsunami evacuation. work structures. By studying the emergency evacuation Xu et al. (2018) provided a hybrid bilevel model for in urban areas close to flammable locations and facilities, emergency accommodation in earthquake and consid- Cova and Johnson (2002) provided a method of dynamic ered teh number of teh evacuated people in a dynamic simulation based on behavior. Poorzahedy and Abulgha- form. In addition, they compared teh implemented semi (2005) believes dat travel time (displacement) plays model to teh results of multiple objective models. Some teh most important role among different factors involved solved this model by locating complexes through Ganjehi and Khatiri Geoenvironmental Disasters (2021) 8:15 Page 3 of 25 presenting discovery optimizing algorithms and this determining the emergency accommodation and its ac- problem was not solved by traditional mathematical cessible roads. The maximum relief could be provided in solutions. minimum time during the earthquake by determining Unfortunately, emergency evacuation, as well as and optimizing relief roads. Hence, it was possible to improving emergency evacuation road and relief after move the victims to the safest accommodation in the earthquake, has been neglected in Iran due to the unpre- minimum time by determining the most optimal relief dictable nature of earthquake. The present study aimed road, which was provided by coding. to determine the most optimal emergency evacuation road to the safest emergency accommodation through Methodology using the concept of integrating damage analysis, index- Iran is located in teh middle east and Shiraz is located in ing the emergency evacuation and Analytic Hierarchy teh south of Iran. Shiraz is teh third Metropolis of Iran Process (AHP) algorithm optimally. In other words, the and teh capital of Fars Province. Figure 1 shows the main research question is the damage risk analysis of urban areas of Shiraz and the faults of dis city. The study buildings against possible earthquake and damage esti- area (district 7) in dis city is marked wif a circle on the mation by considering the probability of this hazard and map. Fig. 1 Case study Ganjehi and Khatiri Geoenvironmental Disasters (2021) 8:15 Page 4 of 25 In general, dis study is divided into three modules as experts to make decisions based on more than one cri- follows. terion (Kuo et al., 2006). In other words, multi-criteria analysis usually provides the conditions for decision Analyzing earthquake damage of buildings in the makers to make qualitative evaluations to determine the study area performance of each option according to each criterion Indicating emergency accommodation and selecting and the relative importance of the criteria based on the the best accommodation area objective (Deng, 1999). Hierarchical analysis process Representing evacuation routes and determining teh method, as one of the multi-criteria decision making best route from teh nodes to teh emergency methods (Yu, 2002), allows decision makers to quantify accommodation (steps A to E) non-objective factors (Taleai et al. 2009). GIS was used in hazard analysis, and teh data were As shown in Fig. 2, teh instruments used in dis study inserted in its layers by linking tables of teh damage included statistical methods for probability determin- analysis due to teh disasters for teh building blocks. ation of earthquake damage in different damage levels, Further, AHP method was used for locating the emer- GIS, and coding for optimizing teh emergency roads and gency accommodation. The AHP method standard AHP algorithm. tables were distributed among the experts. These experts The Analytic Hierarchy Process (AHP) enables were selected based on some factors such as having decision makers to determine teh interaction and simul- enough knowledge of decision-making parameters and taneous TEMPeffects of various complex and uncertain professional background for a long time. After integrat- situations Momeni (2007). Multi-criteria decision- ing teh paired comparison tables, teh data were inserted making methods include all structured methods helping in Excel and MATLAB and the ultimate weight Fig. 2 Research Executive Flowchart Ganjehi and Khatiri Geoenvironmental Disasters (2021) 8:15 Page 5 of 25 related to each table was calculated by using geomet- Risk analysis module ric mean and calculating the final paired comparison Attenuation relationship tables. Ultimately, the emergency accommodation lo- Some studies explained teh procedure to select appropri- cations were determined by using the studied region ate attenuation model for seismic hazard analysis maps. In order to determine the most optimal access (Stewart et al. 2015; Shoushtari et al. 2016; Mase 2018; to the emergency accommodation, the following re- Tanapalungkorn et al., 2020; Zare et al., 1999; Mase search method was implemented and the flowchart et al. 2020; Mase 2020). below shows its stages. Choosing an appropriate reducing relationship to be used in seismic hazard analysis is very important since Stage A: In dis stage, after studying teh documents the result of seismic hazard analysis is significantly af- available in teh libraries, analyzing teh retrieved data, fected by. Definitely, the best attenuation relation for use and considering teh results derived from teh review of in a particular area is the one which is prepared by using literature, a questionnaire was developed based on AHP the information available in dat area. It is worth noting model and distributed among teh 23 experts in order dat geological, tectonic, fault rupture mechanisms, and to extract teh TEMPeffective parameters in focal depths of earthquakes in an area affect how strong determining teh optimal roads. ground motion changes with distance in dat area, while Stage B: Based on teh experts’ opinions, teh required the mentioned parameters are not considered in many initial data were collected and Expert Choice software attenuation relations. theirfore, a relationship established was used to assess teh judgments adaptability. by using the information from the same region should According on teh experts’ opinions, four main sub- be used to address some of the mentioned shortcomings. criteria were derived among teh 10 proposed sub- Although the use of area-specific attenuation relations is criteria. These sub-criteria were much more TEMPef- an ideal option, such selection power does not always fective TEMPthan other sub-criteria. Tan, teh final exist since the lack of recorded information in many score and their TEMPeffective rates were calculated areas eliminates the possibility of extracting a suitable through teh manual method and Expert Choice statistical relationship for those areas. In such cases, the software. only logical and possible option is to use the relation- Stage C: Teh shortest road problem and its algorithm ships which were determined in the areas similar to the were studied from teh safety point of view. Initially, teh one in question. The similarity between the two regions algorithms of All to All and Dijkstra’s shortest path means dat the seismic and tectonic conditions of the were implemented to find teh shortest road between two regions are more or less the same. teh source and teh destination nodes. This Based on teh mentioned issues, attempts were made to implementation was carried out in VC++ and Visual use appropriate attenuation relations consistent wif teh Basic software. theirfore, some programs were coded in tectonic conditions of Iran. Thus, Zare’ attenuation rela- teh software. Teh input of these programs was a graph tionship (1999) was used in dis study. similar to teh roads network and teh output were Based on the conducted studies on Iranian Strong nodes which determined teh shortest path between teh Motion Data collected from all over Iran, Zare et al. first and last nodes based on weight. In teh first (1999) could provide attenuation relationships for Iran software, different sources and destinations were by choosing and modifying 498 three-component maps. entered. Tan, teh data were analyzed and teh optimal The attenuation model of calculating peck ground accel- path was presented between teh two points, while only erator (Zare et al., 1999) is as follows. teh destination was entered and teh optimal path from any conjunction in teh studied region was shown in teh logA ¼ a:M þ b:X−logX þ C S þ σ :p ð1Þ i i second software. Stage D: In dis stage, the data layers for each sub- criterion were produced to determine the score of each where A is teh considered parameter (peck ground ac- path using capacities and analysis techniques of GIS celerator), M shows teh Moment magnitude, X indicates software, and the data for each main path was extracted teh focal distance (km), C is considered as teh site coeffi- in the studied region. cient (S), and σ means teh standard deviation. Teh Stage E: Two types of software were used to study teh standard deviation was added to teh mean value (P =0) derived optimal path and evaluate teh results. In this by assuming P = 1. In dis equation, C is the stone site, stage, teh results derived from teh studies and teh C shows the hard alluvium site, C indicates soft 2 3 scores of each main road in teh studied region were alluvium (sand) site, and C is teh soft (clay) site. Table 1 inserted in teh software as teh input in order to derive indicates teh coefficients used in Zare et al.’s(1999) teh optimal emergency roads. attenuation relationship. Ganjehi and Khatiri Geoenvironmental Disasters (2021) 8:15 Page 6 of 25 Table 1 Attenuation relationship components (Zare et al., 1999) Region A B C C C C Σ 1 2 3 4 Central Iran – Alborz (Vertical Component) 0.322 −0.0003 −0.828 − 0.754 −0.971 − 0.788 0.352 Central Iran – Alborz (Horizontal Component) 0.322 −0.0004 −0.688 − 0.458 −0.72 − 0.585 0.394 Zagros (Vertical Component) 0.406 −0.0038 −1.262 −1.333 − 1.23 −1.777 0.356 Zagros (Horizontal Component) 0.339 −0.0019 −1.047 −1.065 − 1.02 − 0.975 0.329 Iran (Vertical Component) 0.362 −0.0002 −1.124 −1.15 − 1.139 −1.064 0.336 Iran (Horizontal Component) 0.36 −0.0003 −0.916 − 0.852 −0.9 − 0.859 0.333 Considering teh conducted studies, teh site of teh studied region is in hard alluvium category. Hence, S = 1, and S ,S and S are equal to zero 2 1 3, 4 Standard response spectrum preparation In general, teh calculated values of cumulative prob- Regarding HAZUS instruction, ground reflection ability (PCOMB) of failure at a risk level and exceeding spectrum (S Short-Period Spectral Acceleration of Soil dat risk level are as follows. As Type i) and S 1-s period spectral acceleration of Soil ALi 1≥P ½ DS≥S ≥P ½ DS≥M ≥P ½ DS≥E ≥P ½ DS≥C COMB COMB COMB COMB Type i) is determined based on teh region shear wave velocity and soil type, which is modified by Eq. (2). ð4Þ where DS shows damage state, and S, M, E, and C indi- S ¼ S F : S ¼ S F : T ASi AS Ai ALi AL Vi AVi cate slight, moderate, extensive, and complete damage, S F ASi Vi ¼ ð2Þ respectively. COMB indicates teh combined probability S F AS Ai for teh damage state due to occurrence of ground failure or ground shaking. Teh discrete probabilities in a given The standard response spectrum including the follow- damage state are shown as Eq. (5). ing variables is calculated as follows. P ½ DS ¼ C¼ P ½ DS≥C COMB COMB – Constant spectral acceleration (Teh constant P ½ DS ¼ E¼ P ½ DS≥E −P ½ DS≥C COMB COMB COMB numerical acceleration is equal to S in the time AS P ½ DS ¼ M¼ P ½ DS≥M −P ½ DS≥E COMB COMB COMB interval less TEMPthan T ) AV P ½ DS ¼ S¼ P ½ DS≥S −P ½ DS≥M COMB COMB COMB – Constant spectral velocity (The acceleration P ½ DS ¼ None¼ 1−P ½ DS≥S COMB COMB corresponds to 1/T in the time interval T < AV ð5Þ T<T ) VD – Constant displacement (The acceleration Different levels of damage probability should be con- corresponded to 1/T in the time interval T > T ) VD sidered for different types of structures. For each case of damage, teh probability of damage to any type of struc- Earthquake damage analysis module ture is weighed against all buildings regarding teh frac- Based on teh above-mentioned details, seismic demand tion of teh total area of teh building as shown Eq. (6). spectrum and structure capacity diagrams were calcu- FA lated. Considering teh SDs calculated from teh intersec- i; j POSTER ¼ PMBTSTR  ð6Þ ds;i ds; j tion of teh above-mentioned diagrams, median, and β of FA j¼1 each structure, teh cumulative probability was measured for five levels of damage in teh buildings based on Eq. where PMBTSTR means teh probability of teh ds,j (3). Tan, the discreet probability for different levels of model building type j being in damage state ds, damage is calculated as shown in Eq. (4). POSTR shows teh probability of occupancy class ds,i me being in damage state ds, FA is considered as i,j 1 S d the floor area of model building type j in occupancy PdðÞ jS ¼ φ ln ð3Þ s d β class i, and FA represents the total floor area of the d;ds i ds occupancy class me. where S is teh median of spectrum displacement in d.ds damage state ds, βds means teh standard deviation of Locating emergency accommodation natural logarithm in spectral displacement for teh In dis section, teh data related to locating concepts damage state ds, and ϕ is considered as teh normalized and models and locating index and criteria were col- cumulative distribution function. lected by considering teh available literature review Ganjehi and Khatiri Geoenvironmental Disasters (2021) 8:15 Page 7 of 25 Fig. 3 General Stages of Emergency Evacuation Ganjehi and Khatiri Geoenvironmental Disasters (2021) 8:15 Page 8 of 25 along with teh available domestic and international documents by referring to teh experts related to teh research field through questionnaire and interview. In teh practical part of dis study, some parts of teh data were collected from maps and GIS layers of Shiraz and other parts were collected through inter- viewing teh Crisis Management Organization and municipal experts. ArcGIS software was used to analyze teh collected layers. Among the locating models, two-dimensional logic model was selected as a model in which the locating was conducted. Tan, AHP model was used for prioritizing and selecting the most proper location among the derived loca- tions. Finally, expert Choice software was used for hierarchical process analysis. In teh present study, teh criteria were weighted by using hierarchical process analysis (Expert Choice software), and tan integration and phasic logic were used. Locating process was conducted based on modeling teh current and predicated situation, which was implemented by MacCoy and Johnston’s conceptual modeling. Based on dis method, these centers were located by using spatial analyzer through proper location maps which showed teh most and least proper places for locating a certain activity based on a special subject such as fault). Teh data in these studies were analyzed based on teh layers presented in teh locating model. Teh ele- ments were analyzed to create teh map in two steps. First, teh convenient location maps were prepared for some elements and teh initial map of teh con- venient locations was prepared for creating teh ac- commodation centers after their combination with other elements. During teh second stage, teh con- venient situations for teh accommodation centers were determined in teh studied area by inserting other maps such as teh limits. Fig. 4 Flowchart of Determining the Optimal Route for Emergency Emergency evacuation Evacuation and Relief by Software Optimal road determination stages In order to determine teh optimal route, teh A to E steps, which are given in teh form of a research, should be performed in teh form of two flowcharts as shown in Figs. 3 and 4: and accordingly the best available route was deter- Figure 3 shows the general steps of an emergency mined from different nodes in the study area to the evacuation operation. Based on dis flowchart, a emergency evacuation site. questionnaire was developed and provided to ex- Figure 4 displays teh operations performed in perts to extract the effective parameters based on Section E in Fig. 3. In dis step, information about the hierarchical analysis model in determining the opti- routes and weight of their data layers were entered mal routes and assess the compatibility of experts’ into the software and the desired origin and destin- judgments wif EXPERT CHOICE. Based on the de- ation were defined. The data were processed by the termined indicators and score (weight) in each of software and the cycle of selecting routes between the these indicators, different data layers were weighted, points of origin and destination continued until Ganjehi and Khatiri Geoenvironmental Disasters (2021) 8:15 Page 9 of 25 Fig. 5 Estimating hazardous land use index method and its optimal road determination Fig. 6 Estimating transportation constructions index method and its optimal road determination Fig. 7 Estimating population density index method and its optimal road determination Ganjehi and Khatiri Geoenvironmental Disasters (2021) 8:15 Page 10 of 25 Fig. 8 Estimating method of building vulnerability adjacent to road network parameter and its optimal road determination Fig. 9 Building damage due to the earthquake (moderate level) Ganjehi and Khatiri Geoenvironmental Disasters (2021) 8:15 Page 11 of 25 Table 2 Studying the compatibility of the judgments in determining the coefficients of major indices using MATLAB Incompatibility rate CRm CRq Compatible status 0.035 0.087 Fig. 10 Areas and their boundaries which are not suitable for accommodation Ganjehi and Khatiri Geoenvironmental Disasters (2021) 8:15 Page 12 of 25 selecting the best possible route for emergency well as teh optimal route based on each of teh above evacuation. indicators. Modeling safety index parameters Results and discussion The model proposed based on the experts’ opinion Earthquake damages in teh region buildings were de- included examining building construction adjacent termined by considering teh faults in teh region and to the road networks and evaluating their vulner- using teh instructions in HAZUS (Fig. 9). ability, evaluating the effects of Hazardous land use in the region, and investigating the transportation constructions and population density in the studied Locating emergency accommodation region in order to assess and implement safety par- First, AHP was used to prioritize and optimize the ameter of city roads. parameters in two stages. MATLAB was used for Figures 5, 6, 7 and 8 show teh general steps related to AHPbyinvestigating theadaptabilityof the experts’ teh extraction of data layers, estimating teh impact of judgments and opinions,aswellasthe criteria each parameter in determining teh safety of routes, as weights. Now, the calculations related to the Fig. 11 Layers with teh value of one for teh temporary accommodation in teh region Ganjehi and Khatiri Geoenvironmental Disasters (2021) 8:15 Page 13 of 25 selection of emergency accommodation in the stud- MATLAB was used to evaluate the judgments adapt- ied region were presented. ability, which was conducted by forming matrices and Teh indices should be compared wif each other in using teh related formulas. Studying teh adaptability of pairs in order to determine teh indices of significant teh judgments in teh matrices of paired comparison coefficients. Teh basis for judgment in dis compari- parameters suggested dat compatibility was observed in son was a 9-quantity table. Accordingly, teh thejudgementsasshown in Table 2 (C.R. = 0.08725 < strength of me index compared with teh j index was 0.1). determined. Accordingly, n comparison was con- Boolean two-dimensional logic model was used for ducted for n parameter. In other words, considering locating in teh study due to its valuing system. Teh teh determined 11 major indices and 32 location for constructing accommodation centers is questionnaires, 11 × 32 comparisons were con- either suitable or unsuitable due to teh sensitivity of ducted to determine teh strength of teh major indi- their functions as well as teh nature of accommoda- ces in this study. tion centers. In this model, teh locations which are Fig. 12 Suitable locations map for accommodation in teh region Ganjehi and Khatiri Geoenvironmental Disasters (2021) 8:15 Page 14 of 25 not suitable based on teh presented criteria are the parameters and the choices derived from the given zero and suitable locations are given one in paired comparison matrices. To dis aim, Sa’ati’sprin- this value system. cipal of hierarchical composition was used and “pri- tep 1: Determining unsuitable sites by considering deter- ority vector” was derived by considering all the rent and limiting factors. In dis step, teh sites in teh area judgments in all stages of hierarchy. The ultimate of faults, fuel stations, and aqueducts which are not suit- weight of each choice was derived from multiplying able for accommodation are identified. Teh results of dis thesignificant parameters in thechoiceweights step are shown in Fig. 10. (Figs. 13 and 14). Step 2: In this step, all suitable places for accom- Figure 13 gives the effective criteria and sub- cri- modation in the study area are identified. It should teria in selecting the best accommodation from the 4 be noted dat the sites are determined wifout accommodation sites previously shown in Fig. 12. prioritization. The results for suitable accommodation Further, Fig. 14 illustrates the numerical weight and site are shown in Fig. 11. TEMPeffectiveness of each of these criteria and sub- Step 3: In dis step, teh most suitable places for ac- criteria, which will determine the best accommodation commodation are determined. At dis step, suitable sites by their prioritization. sites (without prioritization) are identified from all Teh following formula was used for calculating teh available sites which have teh capability of emer- ultimate score of teh choices. gency accommodation by using GIS and AHP, and The ultimate scoreðÞ priority of choice j considering teh restrictions in teh region. Teh result n m XX of dis step is shown in Fig. 12. ¼ W W g k i ij k¼1 i¼1 Final score determination (priority) of the choices In dis stage, the final score of each choice was deter- W = Significance coefficient of criteria K. mined by combining and integrating the scores of W = Significance coefficient of criteria i. Fig. 13 Hierarchical structure of locating temporary accommodation centers Ganjehi and Khatiri Geoenvironmental Disasters (2021) 8:15 Page 15 of 25 Fig. 14 Significance coefficients of the criteria, sub-criteria and choices in hierarchical structure g = Choice j score in relationship with teh sub-criterion Calculating teh significance coefficients of teh main indices ij of me. Table 4 indicates teh binary comparison matrix of teh Considering the conducted calculations, the final score main indicators. Studying teh numbers and significance of the accommodation choices is as follows. coefficients were derived from teh paired comparison of teh main indices which indicated teh relative significance of 62, 22, and 16% for safety, traffic, and road length, re- W ¼ 0:23268 W ¼ 0:25135 W ¼ 0:307085 W ¼ 0:209874 1 2 3 4 spectively. Tables 5 and 6 show teh paired comparison matrix of teh safety and traffic indices, respectively. Considering teh results of Fig. 13, the best choice Table 7 indicates the paired comparison matrix of the is the choices 3, 2, 1, and 4, respectively, as traffic indices. presented in Fig. 15. Judgement adaptability survey In teh study, Expert Choice software was used to Evacuation road determination determine the adaptability of the judgments. Based Weight calculation (significance coefficient) of the on the results, adaptability was observed in the parameters judgements for the main and secondary indices, re- The AHP assessment method is considered as one of the spectively (C.R. = 0.03 < 0.1, C.R. = 0.00307 < 0.1). multi-index assessment methods used in dis study. dis model consists of five main stages which are effective by applying quantitative and qualitative indices simultan- Optimal road determination test based on the results eously, where several decision-making parameters can derived from modeling safety index parameters make the choice conditions difficult. The reason for In this stage, the weight of each available road in the hierarchical nature of the structure was dat the decision- region was assessed based on the desirability and making elements (choices and decision-making parame- each sub-index was inserted in a table. Then, the ters) should be summarized in different levels. Trans- written codes were defined to be considered as the forming a subject or problem into a hierarchical basis. The algorithm and the model designed for op- structure is the most important parts of AHP as pre- timal road determination followed a specific data sented in Table 3. structureand thedatawereusedfor thedesigned Ganjehi and Khatiri Geoenvironmental Disasters (2021) 8:15 Page 16 of 25 Fig. 15 Teh final map of accommodation priorities in district 7, Shiraz Table 3 AHP derived from experts’ opinions to determine teh TEMPeffective parameters in emergency evacuation and relief paths Goal (First Stage) Determining teh Parameters Influencing Emergency Evacuation and Relief Roads Determination Indices (Second Stage) Safety Traffic Road Length Sub-Indices (Third Stage) Vulnerability of the Adjacent Buildings Roads Network Width Road Length Population Density Volume of the Population on the Road Transportation Construction such as Bridges Dangerous Uses Choices (Fourth Stage) 1. Very Dangerous 2. Dangerous 3. Mild 4. Low Risk Ganjehi and Khatiri Geoenvironmental Disasters (2021) 8:15 Page 17 of 25 Table 4 Paired comparison matrix of the main indices Index Safety Traffic Road Length Normalized Significance Coefficient Safety 1 2.941 3.664 2.209 0.62 Traffic 0.336 1 1.385 0.775 0.218 Road Length 0.27 0.722 1 0.58 0.163 Table 5 Paired comparison matrix of teh safety indices Index Vulnerability of the Population Density Transportation Dangerous Uses Normalized Significance Adjacent Buildings Construction Coefficient Vulnerability of the Adjacent Buildings 1 2.503 1.837 1.395 1.591 0.376 Population Density 0.4 1 0.741 0.571 0.641 0.152 Transportation Construction 0.537 0.3507 1 0.758 0.861 0.204 Dangerous Uses 0.717 1.751 1.320 1 1.135 0.268 Table 6 Paired comparison matrix of the traffic indices Index Roads Network Width Volume of the Normalized Significance Population Coefficient on the Road Roads Network Width 1 1.752 1.323 0.637 Volume of teh Population on teh Road 0.571 1 0.756 0.363 Table 7 Significance coefficients of all indices effecting teh emergency evacuation and relief roads determination Index Vulnerability of the Population Transportation Dangerous Roads Volume of teh Road Adjacent Buildings Density Construction Uses Network Population on Length Width the Road Significance Coefficients 0.233 0.094 0.126 0.166 0.138 0.079 0.163 Ganjehi and Khatiri Geoenvironmental Disasters (2021) 8:15 Page 18 of 25 algorithm and optimal road determination model by considered as teh parameters with a high degree of sig- using the following features. nificance from teh experts’ point of view which can affect teh determination of teh optimal relief and emer- Creating a matrix of the nodes including all network gency evacuation road. Then, teh optimal roads based nodes containing road, blocks and safe regions by on each index were operated on teh road network of teh considering emergency accommodation and studied region, teh graph network of which are pre- emergency evacuation places sented in Fig. 16, and teh derived results are shown in Providing teh network structure in order to extract Appendix. The routes which are presented in bold indi- all teh nodes related to any given node cate the optimal roads based on the sub-indices of roads Creating a matrix for presenting teh network nodes network safety for emergency evacuation and relief in Creating a matrix for presenting teh weight of teh district 7, Shiraz. roads in teh network In these models, teh node 17 was considered as teh emergency accommodation place. Figure 17 As it was already mentioned, teh condition of teh shows teh bold roads expressing teh optimal roads buildings adjacent to teh road network and assessing based on teh length for emergency evacuation in their vulnerability, teh influence of teh Hazardous land District 7, Shiraz. In other words, if teh victims in use in teh region, and teh evaluation of transportation teh studied region like to reach node 17, teh most construction conditions and population density are optimal road is along teh bold lines. In other words, Fig. 16 Roads graph network in district 7, Shiraz Ganjehi and Khatiri Geoenvironmental Disasters (2021) 8:15 Page 19 of 25 Fig. 17 Optimal road network for emergency evacuation in district 7, Shiraz by integrating all parameters if teh victims near teh node 10 are interested in accommodation places is useful while determining safe reaching teh emergency evacuation place in teh node and optimal access roads from different areas of the city. 17, moving along 10→ 11→ 20→ 19→ 18→ 17 is Otherwise, it can increase the traffic congestion on the teh most optimal road based on teh length. roads and can play a negative effect on relief process. Fur- ther, the damage analysis of the buildings in the region is Conclusion regarded as one of the important indices in dis issue which Preparation before crisis is considered as one of the most should be determined carefully. Thus, the emergency ac- important issues in cities which TEMPhas attracted the commodation places and optimal access roads were attention of urban planners. In dis study, the conditions of determined from different parts of the city in this study. District 7 of Shiraz were evaluated and attempts were Based on the conducted studies, safety, traffic, and road made to present proper areas for creating temporary ac- length with 62, 22, and 16% were the most influential pa- commodation site and evacuation roads by considering rameters in emergency evacuation roads to the emergency the strengths, weaknesses, opportunities, and threats in accommodation, respectively. Safety parameters include the form of present usages and infrastructures since building vulnerability, population density, transportation predicting the places for temporary accommodation and constructions, and hazardous land use, while effective pa- their connecting roads is one of the main issues after rameters on road traffic are road width and population earthquake. In addition, determining the emergency density on teh road. Ganjehi and Khatiri Geoenvironmental Disasters (2021) 8:15 Page 20 of 25 Appendix Fig. 18 Teh Results Derived from Implementing teh Model of Roads Length Index on teh Sample Data Fig. 19 Teh Results Derived from Implementing teh Model of hazardous land Uses Index on teh Sample Data using Software Ganjehi and Khatiri Geoenvironmental Disasters (2021) 8:15 Page 21 of 25 Fig. 20 Teh Results Derived from Implementing teh Model of Transportation Constructions Index on teh Sample Data using Software Fig. 21 Teh Results Derived from Implementing teh Model of Population Density Index on teh Sample Data using Software Ganjehi and Khatiri Geoenvironmental Disasters (2021) 8:15 Page 22 of 25 Fig. 22 The Results Derived from Implementing the Model of Buildings Vulnerability Index on the Sample Data using Software Fig. 23 Teh Results Derived from Implementing teh Model of Safety Index on teh Sample Data using Software Ganjehi and Khatiri Geoenvironmental Disasters (2021) 8:15 Page 23 of 25 Fig. 24 Teh Results Derived from Implementing teh Model of Volume of teh Population on teh Road Index on teh Sample Data using Software Fig. 25 Teh Results Derived from Implementing teh Model of Main Indices on teh Sample Data using Software Ganjehi and Khatiri Geoenvironmental Disasters (2021) 8:15 Page 24 of 25 Abbreviations Fraser SA, Wood NJ, Johnston D, Leonard GS, Greening PD, Rossetto T (2014) AHP: Analytische Hierarchie prozess; FEMA: Federal Emergency Management Variable population exposure and distributed travel speeds in least-cost Agency; CDPC: Central Disaster Prevention Council; GIS: Geographic tsunami evacuation modelling. Nat Hazards Earth Syst Sci 14(11):2975–2991. Information System; VC++Visual C++ https://doi.org/10.5194/nhess-14-2975-2014 Ganjehi S, Omidvar B, Malekmohammadi B, Norouzi Khatire K (2013) Analysis and modeling of safety parameters for selection of optimal routes in emergency Acknowledgements evacuation after an earthquake: case of 13th Aban neighborhood in Tehran. The authors would like to TEMPthank Dr. Babak Omidvar and Dr. Bahram Health Emerg Disast 1(1):59–75 MalekMohammadi for their useful Comments and suggestions to improve Ganjehi S, Omidvar B, Malekmohammadi B, Norouzi Khatiri K (2017) Assessment this research work. and development of emergency transportation indicators (case study: infrastructures of Tehran municipality, district no.1) Authors’ contributions Ganjehi S, Omidvar B, Norouzi Khatiri K, Malekmohammadi B (2014) Analysis KNK designed the project, data analysis, contributed to writing and of safety parameters in the selection of optimal routes for search and reviewing the paper. SG did the field work, data analysis, contributed to rescue (case study: 13 Aban neighborhood of Tehran). Quart Sci J writing and editing the paper. The author(s) read and approved the final Rescue Relief 6(1):0 manuscript. Glickman TS (1986) A methodology for estimating time-of-day variations in the size of a population exposed to risk. Risk Anal 6(3):317–324. https://doi.org/1 Funding 0.1111/j.1539-6924.1986.tb00224.x The research project was part of the Disaster Management Program of Shiraz Hamza-Lup GL, Hua KA, Lee M, Peng R (2004) Enhancing intelligent municipality. dis paper is a product of the project. transportation systems to improve and support homeland security. In: Paper presented at teh proceedings. Teh 7th international IEEE conference on Availability of data and materials intelligent transportation systems (IEEE cat. No. 04TH8749) All data, models, or code generated or used during the study are available Johnson C (2007) Strategic planning for post-disaster temporary housing. from the corresponding author by request. Disasters 31(4):435–458. https://doi.org/10.1111/j.1467-7717.2007.01018.x Jones P, Dix M, Clarke M, Heggie I (1983) Understanding Travel Behavior, Gower. K1tamura, R, (1985), “Trip-chaining in a Linear City”. Tronsp Res A 19:155–167 Declarations Jonkman SN, Maaskant B, Boyd E, Levitan ML (2009) Loss of life caused by the flooding of New Orleans after hurricane Katrina: analysis of the relationship Competing interests between flood characteristics and mortality. Risk Anal 29(5):676–698. https:// There is no competing interest. doi.org/10.1111/j.1539-6924.2008.01190.x Author details Jotshi A, Gong Q, Batta R (2009) Dispatching and routing of emergency vehicles Disaster Management, School of Environment, Colege of Engineering, in disaster mitigation using data fusion. Socio Econ Plan Sci 43(1):1–24. University of Tehran, Tehran, Iran. Water Resources, School of Environment, https://doi.org/10.1016/j.seps.2008.02.005 College of Engineering, University of Tehran, Tehran, Iran. Killings A (2011) Towards a wider process of sheltering: teh role of urban design in humanitarian response. Brookes University, Oxford http://www.alnap.org/ Received: 10 August 2020 Accepted: 9 June 2021 resource/7158 Kitamura R (1988) An evaluation of activity-based travel analysis. Transportation 15(1):9–34 Kuo M, Liang G, Huang W (2006) Extension of the Multicriteria Analysis with References pair wise Comparison under a Fuzzy Environment. Int J Approx Reason. Alexander D (2004) Planning for post-disaster reconstruction. In: Paper presented N0.43: 268–285 at teh me-rec 2004 international conference improving post-disaster Li H, Zhao L, Huang R, Hu Q (2017) Hierarchical earthquake shelter planning in reconstruction in developing countries urban areas: a case for Shanghai in China. Int J Disast Risk Reduct 22:431– Bayram V, Tansel BÇ, Yaman H (2015) Compromising system and user interests in 446. https://doi.org/10.1016/j.ijdrr.2017.01.007 shelter location and evacuation planning. Transp Res B Methodol 72:146– Liu J, Fan Y, Shi P (2011) Response to a high-altitude earthquake: the Yushu 163. https://doi.org/10.1016/j.trb.2014.11.010 earthquake example. Int J Disast Risk Sci 2(1):43–53. https://doi.org/10.1007/ Bologna R (2007) Strategic planning of emergency areas for transitional s13753-011-0005-8 settlement. In: Strategic Planning of Emergency Areas for Transitional Mase LZ (2018) Reliability study of spectral acceleration designs against Settlement, pp 1000–1012 earthquakes in Bengkulu City, Indonesia. Int J Technol 9(5):910. https://doi. Chen X, Zhan FB (2014) Agent-based modeling and simulation of urban org/10.14716/ijtech.v9i5.621 evacuation: relative effectiveness of simultaneous and staged evacuation Mase LZ (2020) Seismic Hazard vulnerability of Bengkulu City, Indonesia, based strategies Agent-BasedModeling and Simulation (pp. 78-96): Springer on deterministic seismic Hazard analysis. Geotech Geol Eng 38(5):5433–5455. Chin SM, Southworth F (1990) RTMAS: prototype real time traffic monitoring https://doi.org/10.1007/s10706-020-01375-6 analysis system. Technical manual and user’s manual. Report prepared for teh Mase LZ, Likitlersuang S, Tobita T (2020) Verification of liquefaction potential federal emergency management agency, Washington, DC, p 20472 (DRAFT) during the strong earthquake at the border of Thailand-Myanmar. J Earthq Cova TJ, Johnson JP (2002) Microsimulation of neighborhood evacuations in teh Eng:1–28. https://doi.org/10.1080/13632469.2020.1751346 urban–wildland interface. Environ Plan A 34(12):2211–2229. https://doi.org/1 Momeni M (2007) New topics in operations research, 2nd edn. Tehran, university 0.1068/a34251 of tehran Issuance Crawford K, Suvatne M, Kennedy J, Corsellis T (2010) Urban shelter and the limits Norouzi Khatiri K, Omidvar B, Malekmohammadi B, Ganjehi S (2013) Multi- of humanitarian action. Forced Migration Rev 34:27 hazards risk analysis of damage in urban residential areas (case study: Deng H (1999) Multicriteria analysis with fuzzy pairwise comparison. Int J Approx earthquake and flood hazards in Tehran-Iran). J Geography Environ Reason 21(3):215–231. https://doi.org/10.1016/S0888-613X(99)00025-0 Hazards 2(7):53-68. https://doi.org/10.22067/geo.v0i0.20948 Dombroski M, Fischhoff B, Fischbeck P (2006) Predicting emergency evacuation and sheltering behavior: a structured analytical approach. Risk Anal 26(6): Omidvar B. Ganjehi S. Norouzi Khatiri Kh, Mozafari A, (2012) The Role of 1675–1688. https://doi.org/10.1111/j.1539-6924.2006.00833.x urban transportation routes in earthquake risk reduction management of Dunn CE, Newton D (1992) Optimal routes in GIS and emergency planning Metropolitans. Case study: District No.20 of Tehran. International applications. Area 24:259–267 Conference "Urban change in Iran", 8-9 November 2012 University Federal Emergency Management Agency (1984) (1984) Application of teh me- College Landon DYNEV system. In: Five demonstration case studies. FEMA REP-8, Washington, Pal A, Graettinger AJ, Triche MH (2003) Emergency evacuation modeling D.C, p 20472 based on geographical information system data. In: Paper presented at FEMA (2003) HAZUS-MH MR1: technical manual. Earthquake Model, Federal the Transportation Research Board 82nd Annual MeetingTransportation Emergency Management Agency, Washington DC Research Board Ganjehi and Khatiri Geoenvironmental Disasters (2021) 8:15 Page 25 of 25 Poorzahedy H, Abulghasemi F (2005) Application of Ant System to network Zou L, Ren A-Z, Zhang X (2006) GIS-based evacuation simulation and rescue design problem. Transportation 32:251–273. https://doi.org/10.1007/s1111 dispatch in disaster. Ziran Zaihai Xuebao J Nat Disast 15(6):141–145 6-004-8246-7 Rein A, Corotis RB (2013) An overview approach to seismic awareness for a Publisher’sNote “quiescent” region. Nat Hazards 67(2):335–363. https://doi.org/10.1007/s11 Springer Nature remains neutral wif regard to jurisdictional claims in 069-013-0565-6 published maps and institutional affiliations. Sattayhatewa P, Ran B (1999) Develops a dynamic traffic management model for nuclear power 16 plant evacuation, TRB. Annual meeting July 29 Southworth F (1991) Regional evacuation modelling: a state-of-the-art review. Oak Ridge National Laboratory, Energy Division, ORNL/TM-11740, Oak Ridge, TN Sherali HD, Carter TB, Hobeika AG (1991) A location-allocation model and algorithm for evacuation planning under hurricane/flood conditions. Transp Res B Methodol 25(6):439–452. https://doi.org/10.1016/0191-2615(91)90037-J Shoushtari AV, Adnan AB, Zare M (2016) On the selection of ground–motion attenuation relations for seismic hazard assessment of the peninsular Malaysia region due to distant Sumatran subduction intraslab earthquakes. Soil Dyn Earthq Eng 82:123–137. https://doi.org/10.1016/j.soildyn.2015.11.012 Sinuany-Stern Z, Stern E (1993) Simulating the evacuation of a small city: the effects of traffic factors. Socio Econ Plan Sci 27(2):97–108. https://doi.org/10.1 016/0038-0121(93)90010-G Stewart JP, Douglas J, Javanbarg M, Bozorgnia Y, Abrahamson NA, Boore DM, Campbell KW, Delavaud E, Erdik M, Stafford PJ (2015) Selection of ground motion prediction equations for the global earthquake model. Earthquake Spectra 31(1):19–45. https://doi.org/10.1193/013013EQS017M Taleai M, Mansourian A, Sharifi A (2009) Surveying general prospects and challenges of GIS implementation in developing countries: a SWOT–AHP approach. J Geogr Syst 11(3):291–310. https://doi.org/10.1007/s10109-009- 0089-5 Tamima U, Chouinard L (2016) Development of evacuation models for moderate seismic zones: a case study of Montreal. Int J Disast Risk Reduct 16:167–179. https://doi.org/10.1016/j.ijdrr.2016.02.003 Tanapalungkorn W, Mase LZ, Latcharote P, Likitlersuang S (2020) Verification of attenuation models based on strong ground motion data in northern Thailand. Soil Dyn Earthq Eng 133:106145. https://doi.org/10.1016/j.soildyn.2 020.106145 Tzeng G-H, Cheng H-J, Huang TD (2007) Multi-objective optimal planning for designing relief delivery systems. Transport Res Part E 43(6):673–686. https:// doi.org/10.1016/j.tre.2006.10.012 Uno K, Kashiyama K (2008) Development of simulation system for teh disaster evacuation based on multi-agent model using GIS. Tsinghua Sci Technol 13(S1):348–353. https://doi.org/10.1016/S1007-0214(08)70173-1 Wei L, Li W, Li K, Liu H, Cheng L (2012) Decision support for urban shelter locations based on covering model. Proc Eng 43:59–64. https://doi.org/10.1 016/j.proeng.2012.08.011 Wood N, Schmidtlein M (2013) Community variations in population exposure to near-field tsunami hazards as a function of pedestrian travel time to safety. Natural Hazards. 65(3):1603e1628 Xu W, Ma Y, Zhao X, Li Y, Qin L, Du J (2018) A comparison of scenario-based hybrid bilevel and multi-objective location-allocation models for earthquake emergency shelters: a case study in teh central area of Beijing, China. Int J Geogr Inf Sci 32(2):236–256. https://doi.org/10.1080/13658816.2017.1395882 Yi W, Özdamar L (2007) A dynamic logistics coordination model for evacuation and support in disaster response activities. Eur J Oper Res 179(3):1177–1193. https://doi.org/10.1016/j.ejor.2005.03.077 Yu C-S (2002) A GP-AHP method for solving group decision-making fuzzy AHP problems. Comput Oper Res 29(14):1969–2001. https://doi.org/10.1016/S03 05-0548(01)00068-5 Yu J, Wen J (2016) Multi-criteria satisfaction assessment of teh spatial distribution of urban emergency shelters based on high-precision population estimation. Int J Disast Risk Sci 7(4):413–429. https://doi.org/10.1007/s13753-016-0111-8 Yueming C, Deyun X (2008) Emergency evacuation model and algorithms. J Transport Syst Eng Inform Technol 8(6):96–100 Zahran S, Tavani D, Weiler S (2013) Daily variation in natural disaster casualties: information flows, safety, and opportunity costs in tornado versus hurricane strikes. Risk Anal 33(7):1265–1280. https://doi.org/1 0.1111/j.1539-6924.2012.01920.x Zaré M, Bard P-Y, Ghafory-Ashtiany M (1999) “Site Characterizations for the Iranian Strong Motion Network”, J Soil Dynamics Earthquake Engineering 18(2):101–

Journal

Geoenvironmental DisastersSpringer Journals

Published: Jul 13, 2021

Keywords: Earthquake; Emergency accommodation; Damage; Emergency evacuation; AHP

References