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Background: The Dharamshala region of Kangra valley, India is one of the fastest developing Himalayan city which is prone to landslide events almost around the year. The development is going on a fast pace which calls for the need of landslide susceptibility zonation studies in order to generate maps that can be used by planners and engineers to implement the projects at safer locations. A landslide inventory was developed for Dharamshala with help of the field observations. Based on field investigations and satellite image studies eight casual factors viz. lithology, soil, slope, aspect, fault buffer, drainage buffer, road buffer and land cover were selected to represent the landslide problems of the study area. The research presents the comparative assessment of geographic information system based landslide susceptibility maps using analytical hierarchy process and frequency ratio method. The maps generated have been validated and evaluated for checking the consistency in spatial classification of susceptibility zones using prediction rate curve, landslide density and error matrix methods. Results: The results of analytical hierarchy process (AHP) shows that maximum factor weightage results from lithology and soil i.e. 0.35 and 0.25. The frequency ratios of the factor classes indicate a strong correlation of Dharamsala Group of rock (value is 1.28) with the landslides which also agrees with the results from the AHP method where in the same lithology has the maximum weightage i.e. 0.71. The landslide susceptibility zonation maps from the statistical frequency ratio and heuristic analytical hierarchy process method were classified in to five classes: very low susceptibility, low susceptibility, medium susceptibility, high susceptibility and very high susceptibility. The landslide density distribution in each susceptibility class shows agreement with the field conditions. The prediction rate curve was used for assessing the future landslide prediction efficiency of the susceptibility maps generated. The prediction curves resulted the area under curve values which are 76.77% for analytical hierarchy process and 73.38% for frequency ratio method. The final evaluation of the susceptibility maps was based on the error matrix approach to calculate the area distributed among the susceptibility zones of each map. This technique resulted in assessing the spatial differences and agreement between both the susceptibility maps. The evaluation results show 70% overall spatial similarity between the resultant landslide susceptibility maps. (Continued on next page) * Correspondence: firstname.lastname@example.org Department of Environment Science, School of Earth and Environmental Sciences, Central University of Himachal Pradesh, Shahpur, HP 176206, India Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Sharma and Mahajan Geoenvironmental Disasters (2018) 5:4 Page 2 of 16 (Continued from previous page) Conclusions: Hence it can be concluded that, the landslide susceptibility map (LSM) generated from the AHP and frequency ratio method have yielded good results as the 100% landslide data falls in the high susceptibility and very high susceptibility classes of both the maps. Also, the spatial agreement of almost 70% between the resultant maps increases the reliability on the results in the present study. Therefore, the LSM generated from AHP method with 76.77% landslide prediction efficiency can be used for planning future developmental sites by the area administration. Keywords: Landslide susceptibility mapping, Heuristic and statistical model, Map evaluations Background 2011; Yalcin et al. 2011; Ghosh et al. 2011; Kayastha et al. Landslides are the down slope movement of debris, rocks 2013; Bijukchhen et al. 2013; Anbalagan et al., 2015; Rawat or earth material under the force of gravity (Cruden, et al. 2015; Sharma and Mahajan 2018;Chen et al. 2016). 1991). Destructive mass movements such as landslides are In India landslide susceptibility mapping for Gharwal and considered as a major geological hazard around the globe. Kumaun region of Uttarakhand has been carried out by The Landslide activities in India are mostly associated Pachauri and Pant 1992; Gupta et al. 1999;Anabalgan et with its the northern most states such as Uttarakhand, al., 2008; Anbalagan et al., 2015; Kumar and Anabalgan, Himachal Pradesh, Sikkim and West-Bengal which are lo- 2016 whereas, Sarkar and Kanungo, 2004;Sarkaretal. cated in the Himalayan foothills with dynamic tectonic 2013; Ghosh et al. 2011 have mapped the landslides of and climatic variations (Sarkar et al. 1995; Chauhan et al. Darjeeling Himalaya for statistical correlation with the 2010) and also towards the southern India the Nilgiri causal factors. For dealing with the landslide hazard and range and the Western Ghats are prone to landslides in- its risk imposed on various elements, it is necessary to stead of hard rocks and tectonic stability (Kaur et al. evaluate the correlation of probable causative factors with 2017). According to the Geological survey of India almost the landslide location’s characteristics. The qualitative 15% of the land area in India is exposed to the landslide methods such as AHP subjectively help to rank the causal events (Onagh et al. 2012) and is the worst affected factors leading to classification of an area based on the country by landslides in Asia after China (Guha-Sapir et priority scale whereas, the quantitative methods (bivariate al., 2012; Binh Thai et al. 2016). The tendency towards the or multivariate statistical analysis) use the observed land- landslide is caused by various factors such as the steepness slide data for asserting the spatial relationship of the prob- of slopes, the tectonic conditions of the study area, pro- lem with the prevailing geo-environmental parameters (J. longed rainfall episodes with their return periods, topog- Corominas et al. 2014). For generating a reliable spatial in- raphy and the inherent properties of the slope material, formation regarding a natural hazard, remote sensing data Anbalagan (1992). The mitigation measures for landslides and the geographic information system (GIS) are very require the identification of existing landslides in an area powerful tools (Tofani et al. 2013). The application of GIS for spatial prediction of future events by studying the pre- is useful in processing the digital elevation models for vailing causal factors (Rai et al. 2014) for which a standard extracting information such as: slope angle, aspect, drain- tool known as landslide susceptibility mapping is used age network etc. and to integrate the various thematic around the world by various researchers (Guzzetti et al., layers for generating susceptibility, hazard or risk maps. In 1999; Van Westen et al. 2008). Fell et al. 2008 considered the state of Himachal Pradesh (H.P.) attempts have been the landslide susceptibility for identification of landslide made for landslide susceptibility zoning of the landslide prone sites and their relation to the set of causal factors in prone areas such as district Chamba, Bilaspur and Parwa- that area. The landslide susceptibility mapping generally noo (Sharma and Mehta 2012; Sharma and Kumar 2008) involves two methods (I) qualitative which is based on ex- whereas studies related to the use of statistical modeling pert knowledge and the landslide inventory development methods for susceptibility mapping are lacking for import- (Saha et al., 2002) such as analytical hierarchy process ant areas of this hilly state such as Kangra Valley which (AHP) used by many researchers (Komac 2006; Ghosh et rests at the Himalayan foothills and experiences number al. 2011; Kayastha et al. 2012;Wu et al. 2016; Kumar and of landslide episodes in various parts every year. Some Anabalgan 2016; Achour et al. 2017)(II) quantitative parts of district Kangra, Himachal Pradesh such as Dhar- methods including bivariate and multivariate modeling amshala region is the fastest growing tourism hub which methods for statistical evaluation of landslides occur- has been announced as one of the smart cities of India. The rences (Yin and Yan 1988; Kumar et al. 1993; Anbalagan Dharamshala region is characterized by steeply dipping and Singh 1996; Dai and Lee, 2002;Saha et al. 2005;Lee slopes with number of drainages cutting across its weak and Sambath 2006;Mathew et al. 2007; Dahal et al. 2008; and weathered lithology. The district Kangra, H.P. is tec- Singh et al. 2008; Pradhan and Lee 2010; Rozos et al. tonically very active and has experienced number of Sharma and Mahajan Geoenvironmental Disasters (2018) 5:4 Page 3 of 16 moderate and major earthquakes in past such as 1905 Kan- susceptibility maps for prediction efficiency of future land- gra earthquake (Ms 7.8) which devastated this region badly slide events. The resulting LSMs have been evaluated by (Ghosh and Mahajan 2011). Later on, from 1968 to 1986 the use of landslide density analysis and error matrix tech- the Dharamshala region of district Kangra which is sand- nique in order to check the concordance between the sus- wiched between longitudinal Main Boundary Thrust ceptibility class area distributions from heuristic and (MBT) in the north and Drini Thrust in the south, experi- statistical methods. The evaluation of the LSMs has deter- enced three moderate earthquakes having magnitude vary- mined the total agreement and the spatial difference be- ing between Ms 4.9 to Ms 5.7 (Kumar and Mahajan 1991; tween the maps generated. The results can be useful for Mahajan and Kumar 1994). Thetectonicemplacement and landslide risk assessment studies and for planners in imple- the northward movement of Indian landmass keep the menting developmental projects. Dharamshala region of H.P. under continuous stress condi- tions making it tectonically and geomorphologically dy- Study area and geological setting namic. Mahajan and Virdi (2000) have studied the landslide The study area covers a rectangle of 39.7 sq. km (32°12′ sites of Dharamshala region using the field based mapping and 32° 15′ and N- 76°17′ and 76° 23′ E) as shown in methods and identified 25 major landslides for correlating Fig. 1 with an elevation between 899 m to 2523 m with factors such as slope angle, relief, drainage network a.m.s.l. The geomorphology of the study area is domi- etc. Sharma et al. (2015) have documented a major land- nated with hills and mountains dissected by number of slide event (Tirah lines Landslide) reported as a result of drainages which are locally known as khad. The main very high rainfall in the month of August 2013 which khad flowing are Charan khad at the southern edge, destroyed almost 10 multistoried buildings in army canton- Banoi khad in the middle and the Gaj khad at the north- ment area of Dharamshala. Looking at the past record of ern edge of the study area which are the main tributaries the landslide studies and the structural complexity in the of river Beas in the district Kangra of Himachal Pradesh. Dharamshala region it becomes important to statistically Dharamshala region comes under wet temperate zone analyze the factors playing major role in causing slope in- with mean annual temperature remain between 19 ± stability and in order to minimize their societal impacts by 0.5 °C and the annual precipitation 2900 ± 639 mm developing landslide hazard or susceptibility zonation maps. (Jaswal et al. 2014). Geologically the southern part of the This study involves the landslide susceptibility mapping area falls in the Outer Himalaya comprising the Siwalik (LSM) of Dharamshala region using heuristic judgment (boulder conglomerate exposures), Dharamsala group based analytical hierarchy process (AHP) and statistical fre- and Murree formation (Sandstone, Claystone and quency ratio (FR) method followed by the comparison of mudstone) which is separated by Main Boundary Thrust Fig. 1 Location map of the study area shown as hill shade view with local drainages and some of the major locations Sharma and Mahajan Geoenvironmental Disasters (2018) 5:4 Page 4 of 16 (MBT) from the northern part of the study area com- Google earth imagery. All the prepared thematic maps prising of Lesser Himalayan rocks (Dharamkot Lime- were rasterized at grid size of 30 × 30 with total pixel stone and Chail Formation having low grade count for the study area 44,165 for using in the GIS metamorphic such as slates) as shown in Fig. 2. Most of based modeling methods (AHP and FR). In the analytical the settlements and the road excavation are in the Outer hierarchy process (AHP) method field survey based Himalayan rocks of the study area which are weak and judgments and the data from previous literature have have led to many slope instability conditions in the past. helped in assigning weightage (heuristic) to the causal Weak lithology such as weathered sandstone, claystone factors and the factor classes whereas in the frequency along with unplanned construction activities or excava- ratio (FR) method, the ratio of landslide percentage in a tions of slopes for development projects and the heavy factor class and the percentage area of that factor class rainfall in this area often lead to landslides especially in gave the weightages (statistical). Both the modeling the monsoon season. The slopes of Dharamshala region methods (analytical hierarchy process and frequency ra- are steeply dipping up to > 41° with upper 5 m to 10 m tio) have resulted in landslide susceptibility index (LSI) cover of fluvial deposits or the debris cover which is eas- maps which were reclassed using fivefold classification ily prone to sliding under adverse conditions. for zoning the landslide prone area which is very low susceptibility (VLS), low susceptibility (LS), medium sus- Methods ceptibility (MS), high susceptibility (HS) and very high The present analysis was carried out in three steps: data susceptibility (VHS). Both the landslide susceptibility collection, database generation (thematic maps) and zonation maps (LSZM) were validated using the land- modeling for landslide susceptibility mapping (LSM). slide density distribution method and the prediction Firstly, the study area has been investigated for the pre- curve success rates. The evaluations of the resulting vailing landslide conditions for which a landslide inven- landslide susceptibility maps are based on spatial area tory (Fig. 3) was developed through field surveys and distribution match between the susceptibility classes for available satellite imageries. Thirty nine landslide loca- which the error matrix method has been used. The eval- tions were mapped in the total study area of 39.7 km . uations represent the concordance and the disagreement For the correlation of spatial distributions of the prevail- of class area distribution from the use of heuristic judge- ing landslide with the chosen eight causal factors, vari- ments and the objective datasets. ous thematic maps were developed. With help of ASTERGDEM of 30 m resolution (source- USGS web- Analytical hierarchy process (AHP) site) the drainage buffer, slope angle and the aspect maps Analytical hierarchy process is the decision making for a have been produced whereas the geology, soil and fault complex problem by arranging the elements of that buffer maps were prepared with the help of previous problem in a hierarchy. It is a semi-qualitative process in published maps (Mahajan and Virdi, 2000) and the land which the weightages to the elements are assigned based cover and road buffer maps were extracted with help of on the expert’s judgment and the weightage values vary Fig. 2 Geological Map of Dharamshala region showing lithology and structure exposed (Source- Mahajan and Virdi 2000) Sharma and Mahajan Geoenvironmental Disasters (2018) 5:4 Page 5 of 16 Fig. 3 Landslide Inventory shown in the hill shade map of the study area prepared using the DEM data from 1 to 9 (Saaty, 1980, Saaty and Vargas 2001, Saaty, Random index (RI) is the consistency of the randomly 2005). The standard scale for using AHP method has generated pair wise matrix and is dependent on the size been given in Table 1, according to which factor classes of the matrix as given in Table 2. and the factors are assigned rating with respect to each other. Value 1 is assigned to the class with least influ- Frequency ratio (FR) ence and value 9 is assigned to the class with maximum Frequency ratio modeling is based on correlation of influence. After the weightage assignment the factor landslides in an area with the natural and anthropogenic maps are reclassed and integrated in GIS. causal factors in that area. Mathematically, it is the ratio For checking the consistency of the comparison matrix of the percentage of the factor class (y) and the percent- prepared by rating factors and factor classes against one age of the landslides (x) in that class (Lee and Talib, another, consistency ratio (CR) is used and the CR value 2005; Pradhan, 2010). The correlation factor for FR i.e. below 0. 1 is considered acceptable (Ayalew et al. 2004). x/y (between the landslides and the factors) vary between < 1 to > 1. If the FR value is > 1 then the there exists a high correlation between landslide occurrence CI CR ¼ ð1Þ and the factor class and if the FR is < 1 then the correl- RI ation is weak. All the thematic maps are reclassed according to the FR values for each factor class and then Where CI is the consistency index calculated as: integrated in GIS for generating the landslide suscepti- bility index (LSI) map. CI ¼ Λ max−n=n−1 ð2Þ Landslide inventory Where n is the order of the matrix and max is the For the preparation of landslide inventory field surveys major value of the matrix. have been carried out to demarcate the GPS location and the nature of landslides. The vector points of the Table 1 Scale for Pairwise comparison noted locations were verified using the Google earth Sr No. Scale Description imagery and then imported in GIS for applying the heur- 1. 1 Equally Preferred istic and statistical models. The inventory data was split into training (75%) and testing (25%) groups as shown in 2. 3 Moderately Preferred Fig. 3 for using in modeling and validation phase re- 3. 5 Strongly Preferred spectively. Thirty nine landslides with varying size were 4. 7 Very Strongly Preferred demarcated out of which the largest landslide covers an 5. 9 Extremely Important 2 area of 0.103 km . In total all the landslides cover 2 2 6. Intermediate (2, 4, 6, 8) Preferences made halfway 1.1 km which is 2.7% of the total study area (39.7 km ) between the main integers and the 75% training data of the total inventory landslide Sharma and Mahajan Geoenvironmental Disasters (2018) 5:4 Page 6 of 16 Table 2 Values of random index based on the size f the matrix area covers 0.81 km . Table 3 shows the location and the type of lithology the landslides belong to. Most of the n 1 2 345678910 mapped landslides have got activated in the monsoon RI 0.0 0.0 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.51 season (July to September) in form of debris flow mostly. Some of the landslides show mudflow or earth Table 3 Landslide Inventory Locations Landslide ID GPS Location Lithology Landslide Type 1. 32°13′50.48”N, 76°19′57.4″E Dharamsala Group of Rocks Debris Slide 2. 32°13′27.18”N, 76°19′32.9″E Dharamsala Group of Rocks Mud Flow 3. 32°13′45.2”N, 76°19′08.4″E Dharamsala Group of Rocks Earth Flow 4. 32°13′56.7”N, 76°19′01.60″E Dharamsala Group of Rocks Debris Slide 5. 32°13′9.5”N, 76°18′57.8″E Dharamsala Group of Rocks Mud Flow 6. 32°13′4.5”N, 76°18′29.7″E Dharamsala Group of Rocks Debris Slide 7. 32°14′07.9”N, 76°18′24.7″E Dharamsala Group of Rocks Earth Flow 8. 32°14′53.1”N, 76°19′02.5″E Dharamsala Group of Rocks Mud Flow 9. 32°15′42.5”N, 76°18′02.4″E Dharamsala Group of Rocks Debris Slide 10. 32°14′49.4”N, 76°18′4.05″E Dharamsala Group of Rocks Debris Slide 11. 32°14′48.7”N, 76°18′35.9″E Dharamsala Group of Rocks Debris Slide 12. 32°14′54.6”N, 76°18′29.2″E Dharamsala Group of Rocks Debris Slide 13 32°15′13.8”N, 76°18′10.4″E Dharamsala Group of Rocks Debris Slide 14. 32°15′8.5”N, 76°18′11.9″E, Dharamsala Group of Rocks Mud Flow 15. 32°15′8.4”N, 76°18′12.6″E Dharamsala Group of Rocks Debris Slide 16. 32°15′4.9”N, 76°19′22.3″E Dharamsala Group of Rocks Debris Slide 17. 32°13′16.5”N, 76°20′21.2″E Dharamsala Group of Rocks Debris Slide 18. 32°13′16.6”N, 76°20′22.9″E Dharamsala Group of Rocks Debris Slide 19. 32°13′46.9”N, 76°19′56.4″E Dharamsala Group of Rocks Debris Slide 20. 32°13′52.2”N, 76°19′45.3″E Dharamsala Group of Rocks Debris Slide 21. 32°13′56.8”N, 76°20′3.5″E Dharamsala Group of Rocks Debris Slide 22. 32°13′48.1”N, 76°19′56.4″E Dharamsala Group of Rocks Debris Slide 23. 32°14′5.9”N, 76°19′25.9″E Dharamsala Group of Rocks Debris Slide 24. 32°14′13.8”N, 76°19′25.6″E Dharamsala Group of Rocks Debris Slide 25. 32°14′0.3”N, 76°18′46.2″E Dharamsala Group of Rocks Debris Slide 26. 32°14′6.2”N, 76°18′53.3″E Dharamsala Group of Rocks Mud Flow 27. 32°13′43.4”N, 76°18′38.1″E Dharamsala Group of Rocks Earth Flow 28. 32°14′36.2”N, 76°18′21.6″E Dharamsala Group of Rocks Debris Slide 29. 32°13′43.7”N, 76°18′43.7″E Dharamsala Group of Rocks Debris Slide 30. 32°13′39.1”N, 76°18′37.5″E Dharamsala Group of Rocks Debris Slide 31. 32°13′42.8”N, 76°18′21.3″E Dharamsala Group of Rocks Debris Slide 32. 32°14′34.4”N, 76°18′4.3″E Dharamsala Group of Rocks Debris Slide 33. 32°14′33.5”N, 76°18′58.8″E Dharamsala Group of Rocks Debris Slide 34. 32°14′46.8”N 76°18′32.04″E Dharamsala Group of Rocks Debris Slide 35. 32°14′53.3”N, 76°18′29.06″E Dharamsala Group of Rocks Debris Slide 36. 32°14′2.3”N, 76°18′15.5″E Dharamsala Group of Rocks Debris Slide 37. 32°13′26.8”N, 76°19′6.4″E Dharamsala Group of Rocks Mud Flow 38. 32°13′23.6”N, 76°19′8.5″E Dharamsala Group of Rocks Debris Slide 39 32°13′17.0”N, 76°20′23.9″E Dharamsala Group of Rocks Debris Slide Sharma and Mahajan Geoenvironmental Disasters (2018) 5:4 Page 7 of 16 Fig. 4 Field photographs of few recent landslides in Dharamshala region a Vulnerable slope along Dharamshala - Mecleodganj main road b Chhola landslide along the Charan Khad c Bypass road landslide near Kotwali bazaar d Naddi landslide near Dal lake flow type of mass movement which is due to low near to the major faults in this region. To find the strength of material and water logging during the mon- effect of faults on the mass movement activity a fault soons. Figure 4 shows some of the past landslides of the buffer map was prepared with three classes showing Dharamshala that have caused notable destruction. the proximity of 1000 m, 2000 m and 3000 m. There exists no set rules for considering the trigger 3. Distance from road (Road buffer): In hilly areas factors in landslide susceptibility mapping, rather the slope excavation for road widening is a common study area characteristics and the data availability guide practice which greatly influences the slope stability the choice of thematic layers to be used (Ayalew and and similar has been found for the present study Yamagishi, 2005). Based on the study area characteris- area where landslides associated with the slope tics, eight parameters discussed below have been consid- excavation are common. Considering it one of ered as the major causal factors for landslides in the the main causal factors a road buffer map was Dharamshala region and their thematic maps as shown prepared with buffer zones of 200 m, 800 m, in Fig. 5 have been prepared at grid size of 30 × 30 with 1500 m and 2500 m. pixel count of 44,165 in each map for modeling in GIS. 4. Lithology: Lithology of an area is closely related to the landslide occurrence as the strength of the 1. Distance from drainage (Drainage buffer): The emplaced lithology influences the slope stability. In Dharamshala region has a dense drainage network Dharamshala region the lithology is grouped in to as shown in the location map (Fig. 1) and some of four classes which are Dharamsala Group the landslides mapped during the field survey were (sandstone, claystone and mudstone), Siwalik Group found in the vicinity of local drainages. To find the (boulder conglomerates), Dharamkot Limestone and distribution of landslides with respect to the Chail formation (Schist, Quartzite and Gneissic rocks). drainages flowing in the study area, a drainage buffer According to the landslide inventory data all the map or distance from the drainage map was landslides are located in the weak lithology of prepared at proximity of 100 m, 500 m and 1000 m. Dharamsala Group of rocks. 2. Distance from fault (Fault buffer): The emplacement 5. Soil: This parameter includes the overlying cover on of faults in the study area has been found affecting the lithology which has a varying thickness in the the slope stability as many landslides were found present study area and has been grouped into three Sharma and Mahajan Geoenvironmental Disasters (2018) 5:4 Page 8 of 16 Fig. 5 Maps of the chosen causal factors (1) Lithology (2) Land cover (3) Aspect (4) Slope (5) Fault buffer (6) Road buffer (7) Drainage buffer (8) Soil type Sharma and Mahajan Geoenvironmental Disasters (2018) 5:4 Page 9 of 16 classes: debris, clay soil and compact alluvial impact of anthropogenic activities on and near steep deposits. sloping areas. Among the classes for the slope factor, classes 6. Land cover: The study area has been divided into with 16° - 25° and 26° - 35° slope angles (moderate and four classes: forest cover, settlement on low to steep slopes) show maximum FR values 1.48 and 1.35 moderate slopes, sparsely vegetated area and respectively and collectively include more than 60% of the settlement on steep slopes. landslide area. Among the aspects the north-east class with 7. Slope angle: The slope map was extracted from the 18.84% landslide area and the north aspect with 8.89% DEM (30 m resolution) and classified into five landslide area show maximum FR values 1.34 and 1.10 classes: 0° - 5°, 6° - 15°, 16° - 25°, 26° - 35° and ≥ 35°. which indicates more exploitation of north facing slopes or These classes represent the slope inclinations high subsurface moisture conditions due to less sun expos- throughout the Dharamshala area. ure of northern slopes which makes them unstable. None- 8. Aspect: After the slope extraction slope aspects were theless the south facing slopes (SW aspect with 14.76% extracted using GIS tool and was grouped into nine landslide area) have also shown a high correlation FR value classes: flat (− 1), N (0° – 22.5° and 337.5° - 360°), NE but the maximum FR values for northern slopes are an in- (22.5° – 67.5°), E (67.5° -112.5°), SE (112.5° – 157.5°), S teresting parameter showing high anthropogenic interfer- (157.5° – 202.5°), SW (202.5° – 247.5°), W (247.5° – ence. The debris soil hosts all the inventory landslides 292.5°) and NW (292.5° – 337.5°). (100%) and gives the maximum correlation value 1.33 which is indicative of a shallow nature of maximum mass As described in the section 2.1 and 2.2 analytical hier- movements here on the steep slopes. The debris layer com- archy process (AHP) and the frequency ratio (FR) poses the weathered lithology from the Dharamsala Group methods were applied on the causal factors and the (mudstone, sandstone, claystone) and the overlying fluvial factor classes for assigning weightages of influence and deposits. The drainages have shown a higher correlation the frequency ratio for finding correlation with the pre- (FR = 1.01) at 500 m proximity with 62.96% landslide area vailing landslide conditions of the study area. The results whereas for the faults the proximity of 1000 m with 80.17% have been presented in Table 4 (AHP) and Table 5 (FR) landslide area and 2000 m seem critical in term of FR of section 3 respectively. values 1.16 and 0.75 respectively. The road buffer factor shows high landslide activity associated within 200 m prox- Results and discussion imity (FR = 1.51) with more than 50% of the landslide area, In order to combine all the factor maps reclassed with which indicated direct impact of slope excavation for road their weightage values using AHP and frequency ratio widening in the hilly areas. The resulting Fig. 6a shows (FR) values, the map algebra tool was used which landslide susceptibility zonation (LSZ) map from the resulted the two-landslide susceptibility index (LSI) statistical frequency ratio (FR) method with classes: very maps from both the models. The results of AHP com- low susceptibility (VLS), low susceptibility (LS), medium parison matrix in the Table 4 show that maximum factor susceptibility (MS), high susceptibility (HS) and very high weightage results from lithology and soil i.e. 0.35 and susceptibility (VHS). Therefore, a five-fold classification 0.25 respectively followed by the weightages of land scheme was followed based on natural break classifier op- cover (0.14), drainage (0.07) and slope (0.06) whereas tion in GIS, which maximizes the variance between the sus- factors such as road, fault and aspect show a little ceptibility classes and represents a clear trend of class index influence on the landslide occurrences. Figure 6b shows value distribution. The classifications of resulting landslide the landslide susceptibility zonation (LSZ) map resulted susceptibility index maps were carried out in such a way so from the heuristic analytical hierarchy process (AHP) that 20% of the LSZ map area using AHP and FR includes method which has been classified using fivefold 97% and 76% of the landslides respectively. Table 6 shows classification: very low, low, medium, high and very high. the distribution of landslides in various susceptibility classes Table 5 showing the frequency ratios of the factor classes from heuristic and the statistical methods applied, which indicates a strong correlation of Dharamsala Group of shows 18.48 km area in high susceptibility class and rock (FR value is 1.28) with the landslides which also 4.31 km area in very high susceptibility class using AHP agrees with the results from the AHP method where in method whereas using the statistical method (FR) 9.3 km the lithology factor the maximum weightage has been and 13.07 km falls under high and very high susceptibility given to the Dharamsala Group i.e. 0.71. Among the class respectively. land cover classes, the settlements on steep angled slopes have the maximum FR value 6.51 indicating Comparative assessments of LSZ maps from AHP major concentration of landslide sites in this class as and FR 32.7% of the landslide area alone falls in this class which For checking the reliability of the LSZ maps and com- covers only 5.04% of the total map area, which indicates an paring their performance for future landslide prediction Sharma and Mahajan Geoenvironmental Disasters (2018) 5:4 Page 10 of 16 Table 4 Comparison matrix of factor classes and the factors based on analytical hierarchy process (AHP) Factors and Classes 12345 6789 Normalized Eigen Weight Factor Classes Comparisons Lithology Dharamsala Group 1 0.71 Siwalik 0.14 1 0.16 Chail 0.13 0.33 1 0.07 Limestone Formation 0.13 0.33 1 1 0.07 CR = 0.043 Drainage Buffer 100 1 0.25 500 3 1 0.68 1000 0.25 0.11 1 0.07 CR = 0.009 Slope 0° - 5° 1 0.04 6° > 15° 2 1 0.07 16° - 25° 6 4 1 0.31 26°-35° 6411 0.36 > 35° 7 5 0.5 0.33 1 0.22 CR = 0.048 Land Cover Forest 1 0.10 Sparsely vegetated area 2 1 0.16 Settlement on low to moderate slopes 0.33 0.25 1 0.05 Settlement on steep slopes 7691 0.68 CR = 0.049 Fault Buffer 1000 1 0.72 2000 0.25 1 0.22 3000 0.11 0.25 1 0.07 CR = 0.038 Soil Debris 1 0.76 Clay soil 0.11 1 0.08 Compact Alluvial deposits 0.2 2 1 0.16 CR = 0.001 Road Buffer 200 1 0.62 800 0.25 1 0.27 1500 0.13 0.17 1 0.07 2500 0.11 0.14 0.5 1 0.04 CR = 0.07 Aspect Flat 1 0.04 North 6 1 0.20 Sharma and Mahajan Geoenvironmental Disasters (2018) 5:4 Page 11 of 16 Table 4 Comparison matrix of factor classes and the factors based on analytical hierarchy process (AHP) (Continued) Factors and Classes 12345 6789 Normalized Eigen Weight North East 8 5 1 0.34 East 4 0.33 0.25 1 0.11 South East 0.25 0.25 0.17 0.17 1 0.03 South 1 0.33 0.2 0.2 3 1 0.04 South West 5 0.25 0.25 2 6 5 1 0.14 West 4 0.33 0.25 1 5 3 0.33 1 0.09 North West 0.5 0.2 0.17 0.17 0.5 0.5 0.2 0.33 1 0.02 CR = 0.09 Factor Comparison Fault Buffer 1 0.04 Drainage buffer 2 1 0.07 Road Buffer 2 0.5 1 0.06 Land Cover 3341 0.14 Lithology 76551 0.35 Soil 76530.5 1 0.25 Slope 2 0.5 0.5 0.33 0.2 0.33 1 0.06 Aspect 0.17 0.2 0.2 0.14 0.11 0.13 0.17 1 0.02 CR = 0.07 spatially, various techniques have been proposed like under curve (AUC) values which indicate the model agreed area analysis, prediction rate curve, landslide fitness for landslide prediction in which value below 0.5 density distribution etc. (Kayastha et al. 2013; Gupta et refers low accuracy level whereas value from 0.5 to 1 al. 2008). In the present study landslide density in the refers higher accuracy of the models used. In this study susceptibility zones, prediction rate curves and the error both the model heuristic (AHP) and statistical (FR) have matrix method have been used for assessment and the shown AUC value above 0.5, where AHP method gave evaluation of LSZ maps (heuristic and statistical) with 76.77% (0.76) AUC and the FR method gave 73.38% respect to each other. Table 6 shows the landslide dens- (0.73) AUC. These results show that both the methods ity distribution among the susceptibility classes which have given a good prediction rate for estimating the fu- was computed using the ratio of the landslide area in a ture landslide probabilities spatially. susceptibility class to the area of that susceptibility class. The density should increase from the low to very high Evaluation of susceptibility zonation maps susceptibility class (Gupta et al. 2008) which is true for Both comparison method: landslide density distribution the present study. In case of AHP method the high (HS) and prediction rate curve have shown that AHP and FR and very high susceptibility (VHS) class have density techniques gave interpretations on a positive side but value 0.022 and 0.095 respectively whereas, for the FR there exists difference in the results of both the LSZ method the HS and VHS class have density value 0.004 maps generated i.e. the area of each susceptibility class and 0.059 respectively. Therefore, the susceptibility of varies in the maps from AHP and FR method. The various zones in both the maps matched the inventory spatial differences between the susceptibility classes can data distribution noted from the field studies and also help to evaluate the LSMs and can state that, how the both the LSZ maps show a reliable similarity with choices of subjective and objective judgements in heuris- varying values of landslide density distribution. tic and statistical methods respectively influence our The validation of the susceptibility maps from AHP results. To analyze the spatial difference among landslide and FR technique was carried out using the prediction susceptibility classes an error matrix method was used rate curve which computed the cumulative percentage (Gupta et al., 2008; Kayastha et al. 2012) which is pre- of landslide occurrences (testing data) in both suscepti- sented in Table 7. Using the combination of AHP-FR bility zonation maps (Sarkar et al. 2013) which is shown maps error matrix was tabulated showing a high degree in Fig. 7. The prediction curves were analyzed using area match between areas of VLS, LS and MS zones of both Sharma and Mahajan Geoenvironmental Disasters (2018) 5:4 Page 12 of 16 Table 5 Frequency ratio values for the factor classes zone whereas for FR map 13.07 km area is covered in VHS zone but, in total more than 55% of both the map’s Sr No. Factor Class Landslide Class Frequency grid % (x) % (y) Ratio (x/y) susceptibility classes show spatial agreement. These dif- 1 Lithology Dharamshala 100 78.37 1.28 ferences in the areas of HS and VHS class can be due to Group difference of the methods used for susceptibility map- Siwalik Group 0 9.10 0 ping where in the AHP method subjective judgment ap- Chail 0 4.46 0 proach was used for determining the factor weightages whereas in the FR method a bivariate statistical Limestone 0 8.08 0 approach was used to compute weights of each class 2 Land Cover Forest 26.75 25.39 1.05 separately. This evaluation has also helped to analyze the Sparsely Vegetated 40.13 35.58 1.12 agreement of area distribution (pixels or km ) in the Area resulting LSMs which ascertains the consistency of Settlement on low 0.32 33.99 0.009 causal factors used in the study whereas, the disagree- to moderate slopes ment of area distribution refers to the difference of Settlement on 32.78 5.04 6.51 techniques used. Nonetheless, 100% of the observed steep slopes landslide area falls in the high susceptibility and very 3 Slope 0° - 5° 17.05 24.40 0.69 high susceptibility classes which shows good prediction 6° - 15° 11.91 21.45 0.55 rate of both LSZ maps. 16° - 25° 36.54 24.58 1.48 26° - 35° 27.00 19.91 1.35 Conclusions ≥ 35° 7.50 9.66 0.77 The findings in this study point out the following conclusions: 4 Aspect Flat 10.20 11.07 0.92 North 8.89 8.06 1.10 1) The work shows a comparative study of GIS based North East 18.84 14.05 1.34 heuristic and statistical models for landslide East 8.40 8.26 1.01 susceptibility zonation of Dharamshala region of South East 13.70 16.51 0.82 Himachal Pradesh, India. The lithology and the land South 7.91 8.64 0.91 cover factors have shown maximum contribution toward landslide occurrence based on the computed South West 14.76 14.07 1.04 weightage values using AHP and FR models. The West 8.32 8.17 1.01 anthropogenic interferences in this hilly terrain have North West 8.97 11.16 0.80 caused huge impact on the slopes and the condition 5 Soil Type Debris 99.92 75.09 1.33 is worsened as the internal properties of the lithology Clay soil 0.00 18.67 0 and the overlying debris material are weak due to Compact Alluvial 0.08 6.24 0.01 which instability of slopes is triggered. Maximum Deposits landslide locations were mapped in close proximity 6 Drainage 100 m 37.03 37.62 0.98 of the roads and the local drainages. Buffer 500 m 62.96 61.98 1.01 2) The landslide susceptibility zonation maps from both the methods have been classified into five zones: very low 1000 m 0 0.38 0 susceptibility (VLS), low susceptibility (LS), medium 7 Fault Buffer 1000 m 80.17 68.74 1.16 susceptibility (MS), high susceptibility (HS) and very 2000 m 19.82 26.20 0.75 high susceptibility (VHS). Both the LSZ maps show a 3000 m 0 5.05 0 good model fitness for predicting future landslide 8 Road Buffer 200 m 55.70 36.78 1.51 locations based on prediction rate curve method. 800 m 44.29 43.42 1.01 Landslide density distribution increases from low to very high susceptibility class of both the LSZ maps which 1500 m 0 16.84 0 represents an agreement with the field conditions 2500 m 0 2.95 0 of the study area. Such results have inferred a statistical similarity between both the resultant susceptibility maps. the LSZ maps which indicates a similarity of 16.95 km 3) The LSMs prepared have been evaluated to check area in total which constitutes 42.6% of the total map the consistency of area distribution among the areas. In case of high susceptibility zone (HS) and very susceptibility classes from AHP and FR technique. high susceptibility (VHS) zone the difference in the areas The evaluation of the susceptibility maps was based is more i.e. for AHP 4.32 km area is covered in VHS on the error matrix method which resulted into Sharma and Mahajan Geoenvironmental Disasters (2018) 5:4 Page 13 of 16 Fig. 6 Landslide susceptibility zonation maps overlain with the mapped landslides in the study area: a LSM from Frequency Ratio model (b) LSM from Analytical Hierarchy Process model Table 6 Shows the landslide area along with the landslide density distribution in the susceptibility classes of LSZ maps S No. Analytical Hierarchy Process (AHP) 2 2 Class Pixel Count Class Area (km ) Landslide Area (km ) (training data) Landslide Density I. VLS 1509 1.35 0 0 II. LS 8700 7.83 0 0 III. MS 8624 7.76 0 0 IV. HS 20,535 18.48 0.408 0.022 V. VHS 4797 4.31 0.41 0.095 Frequency Ratio (FR) 2 2 Class Pixel Count Class Area (km ) Landslide Area (km ) (training data) Landslide Density I. VLS 1701 1.53 0 0 II. LS 9410 8.46 0 0 III. MS 8186 7.36 0 0 IV. HS 10,341 9.3 0.045 0.004 V. VHS 14,527 13.07 0.77 0.059 Sharma and Mahajan Geoenvironmental Disasters (2018) 5:4 Page 14 of 16 (VHS) classes and the spatial agreement between both the resultant maps as evaluated from error matrix method (Table 7) is more than 70%. Therefore, the maps landslide susceptibility maps generated can prove to be reliable and helpful in the landslide risk assessment for Dharamshala region and can guide planners for implementing developmental projects at safer locations. Abbreviations Asia-Pac: Pacific; Comput Geosci: Computers and geoscience; Comput Intel Sys.: Computational intelligence research; Curr. Sci.: Current science; Eng. Geol.: Engineering geology; Environ: Environment; Environ: Environmental; Geoenviron: Geoenvironmental; Geol. Soc.: Geological society; Geophys.: Geophysical; Int J Appl Obs Geoinf.: International journal applied observation and Geoinformation; J Geosci: Journal of geoscience; J Sci Ind Res: Journal of scientific and industrial research; J: Journal; Jour. Him. Geol.: Journal of Himalayan Geology; Jour.: Journal; Mt Res Dev: Mountain Fig. 7 Graph representing prediction rate curves of statistical model research development; Nat: natural; rem sens: Remote sensing; Sci.: Science; FR (Red trend line) and AHP (Blue trend line) for interpretation of Spat. Inf. Res.: Spatial information research; Theor. Appl. Climatol: Theoretical model fitness for landslide susceptibility mapping and their respective AUC values Acknowledgements Authors Prof. A.K. Mahajan and Mrs. Swati Sharma are thankful to Department of Science and Technology (DST) for all the research facilities differences and the similarities of area (km ) provided under the project no. NRDMS/11/3023/013(G) for carrying out the studies. Department of Earth and Environmental sciences, Central university assigned to each susceptibility zone. The results of Himachal Pradesh is also acknowledged for providing all the resources. have shown a good consistency in the spatial area distribution in very low, low and medium Funding susceptibility classes of LSZ maps which count for Project no. NRDMS/11/3023/013(G) funded by department of Science and Technology (DST), India. 42.6% of the susceptibility map areas. For the high and very high susceptibility classes the spatial area Availability of data and materials distribution in both the LSZ maps varies to some Entire data prepared from this work is presented in the main manuscript. extent but this difference factor is hindered as both Authors’ contributions these classes HS and VHS include 100% landslide SS has carried out the field investigations and preparation of the thematic affected area in each resulting LSZ map. The spatial maps with AKM for developing the landslide inventory. AKM has helped difference of susceptibility classes can be attributed to conceptualize the methodology and SS has drafted the entire manuscript. Both the authors have read and approved the manuscript. to the variance of procedure [subjective (AHP) and objective (FR)] in weighting the factors and their Competing interests classes whereas, the spatial similarity of the The authors declare that they have no competing interests. susceptibility zones may have occurred due to the use of similar causal factors and the landslide Publisher’sNote inventory data for both the modeling methods. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 4) The results from the final map evaluations indicate that the 100% landslide data falls in the high Author details susceptibility (HS) and very high susceptibility Department of Environment Science, School of Earth and Environmental Sciences, Central University of Himachal Pradesh, Shahpur, HP 176206, India. Wadia Institute of Himalayan Geology, Dehradun, India. Table 7 Shows the error matrix for computing spatially agreed area between the landslide susceptibility classes in AHP and FR Received: 25 October 2017 Accepted: 9 March 2018 LSZ maps Landslide VLS LS MS HS VHS Area (km )FR References Susceptibility Class Achour, Y., A. Boumezbeur, R. Hadji, A. Chouabbi, V. Cavaleiro, and E.A. Bendaoud. VLS 1.36 .78 .94 0 0 1.53 2017. 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Geoenvironmental Disasters – Springer Journals
Published: Dec 1, 2018
Keywords: Environment, general; Earth Sciences, general; Geography, general; Geoecology/Natural Processes; Natural Hazards; Environmental Science and Engineering
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