Background

Landslides are the down slope movement of debris, rocks or earth material under the force of gravity (Cruden, 1991). Destructive mass movements such as landslides are considered as a major geological hazard around the globe. The Landslide activities in India are mostly associated with its the northern most states such as Uttarakhand, Himachal Pradesh, Sikkim and West-Bengal which are located in the Himalayan foothills with dynamic tectonic and climatic variations (Sarkar et al. 1995; Chauhan et al. 2010) and also towards the southern India the Nilgiri range and the Western Ghats are prone to landslides instead of hard rocks and tectonic stability (Kaur et al. 2017). According to the Geological survey of India almost 15% of the land area in India is exposed to the landslide events (Onagh et al. 2012) and is the worst affected country by landslides in Asia after China (Guha-Sapir et al., 2012; Binh Thai et al. 2016). The tendency towards the landslide is caused by various factors such as the steepness of slopes, the tectonic conditions of the study area, prolonged rainfall episodes with their return periods, topography and the inherent properties of the slope material, Anbalagan (1992). The mitigation measures for landslides require the identification of existing landslides in an area for spatial prediction of future events by studying the prevailing causal factors (Rai et al. 2014) for which a standard tool known as landslide susceptibility mapping is used around the world by various researchers (Guzzetti et al., 1999; Van Westen et al. 2008). Fell et al. 2008 considered the landslide susceptibility for identification of landslide prone sites and their relation to the set of causal factors in that area. The landslide susceptibility mapping generally involves two methods (I) qualitative which is based on expert knowledge and the landslide inventory development (Saha et al., 2002) such as analytical hierarchy process (AHP) used by many researchers (Komac 2006; Ghosh et al. 2011; Kayastha et al. 2012; Wu et al. 2016; Kumar and Anabalgan 2016; Achour et al. 2017) (II) quantitative methods including bivariate and multivariate modeling methods for statistical evaluation of landslides occurrences (Yin and Yan 1988; Kumar et al. 1993; Anbalagan and Singh 1996; Dai and Lee, 2002; Saha et al. 2005; Lee and Sambath 2006; Mathew et al. 2007; Dahal et al. 2008; Singh et al. 2008; Pradhan and Lee 2010; Rozos et al. 2011; Yalcin et al. 2011; Ghosh et al. 2011; Kayastha et al. 2013; Bijukchhen et al. 2013; Anbalagan et al., 2015; Rawat et al. 2015; Sharma and Mahajan 2018; Chen et al. 2016). In India landslide susceptibility mapping for Gharwal and Kumaun region of Uttarakhand has been carried out by Pachauri and Pant 1992; Gupta et al. 1999; Anabalgan et al., 2008; Anbalagan et al., 2015; Kumar and Anabalgan, 2016 whereas, Sarkar and Kanungo, 2004; Sarkar et al. 2013; Ghosh et al. 2011 have mapped the landslides of Darjeeling Himalaya for statistical correlation with the causal factors. For dealing with the landslide hazard and its risk imposed on various elements, it is necessary to evaluate the correlation of probable causative factors with the landslide location’s characteristics. The qualitative methods such as AHP subjectively help to rank the causal factors leading to classification of an area based on the priority scale whereas, the quantitative methods (bivariate or multivariate statistical analysis) use the observed landslide data for asserting the spatial relationship of the problem with the prevailing geo-environmental parameters (J. Corominas et al. 2014). For generating a reliable spatial information regarding a natural hazard, remote sensing data and the geographic information system (GIS) are very powerful tools (Tofani et al. 2013). The application of GIS is useful in processing the digital elevation models for extracting information such as: slope angle, aspect, drainage network etc. and to integrate the various thematic layers for generating susceptibility, hazard or risk maps. In the state of Himachal Pradesh (H.P.) attempts have been made for landslide susceptibility zoning of the landslide prone areas such as district Chamba, Bilaspur and Parwanoo (Sharma and Mehta 2012; Sharma and Kumar 2008) whereas studies related to the use of statistical modeling methods for susceptibility mapping are lacking for important areas of this hilly state such as Kangra Valley which rests at the Himalayan foothills and experiences number of landslide episodes in various parts every year. Some parts of district Kangra, Himachal Pradesh such as Dharamshala region is the fastest growing tourism hub which has been announced as one of the smart cities of India. The Dharamshala region is characterized by steeply dipping slopes with number of drainages cutting across its weak and weathered lithology. The district Kangra, H.P. is tectonically very active and has experienced number of moderate and major earthquakes in past such as 1905 Kangra earthquake (Ms 7.8) which devastated this region badly (Ghosh and Mahajan 2011). Later on, from 1968 to 1986 the Dharamshala region of district Kangra which is sandwiched between longitudinal Main Boundary Thrust (MBT) in the north and Drini Thrust in the south, experienced three moderate earthquakes having magnitude varying between Ms 4.9 to Ms 5.7 (Kumar and Mahajan 1991; Mahajan and Kumar 1994). The tectonic emplacement and the northward movement of Indian landmass keep the Dharamshala region of H.P. under continuous stress conditions making it tectonically and geomorphologically dynamic. Mahajan and Virdi (2000) have studied the landslide sites of Dharamshala region using the field based mapping methods and identified 25 major landslides for correlating with factors such as slope angle, relief, drainage network etc. Sharma et al. (2015) have documented a major landslide event (Tirah lines Landslide) reported as a result of very high rainfall in the month of August 2013 which destroyed almost 10 multistoried buildings in army cantonment area of Dharamshala. Looking at the past record of the landslide studies and the structural complexity in the Dharamshala region it becomes important to statistically analyze the factors playing major role in causing slope instability and in order to minimize their societal impacts by developing landslide hazard or susceptibility zonation maps. This study involves the landslide susceptibility mapping (LSM) of Dharamshala region using heuristic judgment based analytical hierarchy process (AHP) and statistical frequency ratio (FR) method followed by the comparison of susceptibility maps for prediction efficiency of future landslide events. The resulting LSMs have been evaluated by the use of landslide density analysis and error matrix technique in order to check the concordance between the susceptibility class area distributions from heuristic and statistical methods. The evaluation of the LSMs has determined the total agreement and the spatial difference between the maps generated. The results can be useful for landslide risk assessment studies and for planners in implementing developmental projects.

Study area and geological setting

The study area covers a rectangle of 39.7 sq. km (32°12′ and 32° 15′ and N- 76°17′ and 76° 23′ E) as shown in Fig. 1 with an elevation between 899 m to 2523 m a.m.s.l. The geomorphology of the study area is dominated with hills and mountains dissected by number of drainages which are locally known as khad. The main khad flowing are Charan khad at the southern edge, Banoi khad in the middle and the Gaj khad at the northern edge of the study area which are the main tributaries of river Beas in the district Kangra of Himachal Pradesh. Dharamshala region comes under wet temperate zone with mean annual temperature remain between 19 ± 0.5 °C and the annual precipitation 2900 ± 639 mm (Jaswal et al. 2014). Geologically the southern part of the area falls in the Outer Himalaya comprising the Siwalik (boulder conglomerate exposures), Dharamsala group and Murree formation (Sandstone, Claystone and mudstone) which is separated by Main Boundary Thrust (MBT) from the northern part of the study area comprising of Lesser Himalayan rocks (Dharamkot Limestone and Chail Formation having low grade metamorphic such as slates) as shown in Fig. 2. Most of the settlements and the road excavation are in the Outer Himalayan rocks of the study area which are weak and have led to many slope instability conditions in the past. Weak lithology such as weathered sandstone, claystone along with unplanned construction activities or excavations of slopes for development projects and the heavy rainfall in this area often lead to landslides especially in the monsoon season. The slopes of Dharamshala region are steeply dipping up to > 41° with upper 5 m to 10 m cover of fluvial deposits or the debris cover which is easily prone to sliding under adverse conditions.

Fig. 1
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Location map of the study area shown as hill shade view with local drainages and some of the major locations

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Fig. 2
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Geological Map of Dharamshala region showing lithology and structure exposed (Source- Mahajan and Virdi 2000)

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Methods

The present analysis was carried out in three steps: data collection, database generation (thematic maps) and modeling for landslide susceptibility mapping (LSM). Firstly, the study area has been investigated for the prevailing landslide conditions for which a landslide inventory (Fig. 3) was developed through field surveys and available satellite imageries. Thirty nine landslide locations were mapped in the total study area of 39.7 km2. For the correlation of spatial distributions of the prevailing landslide with the chosen eight causal factors, various thematic maps were developed. With help of ASTERGDEM of 30 m resolution (source- USGS website) the drainage buffer, slope angle and the aspect maps have been produced whereas the geology, soil and fault buffer maps were prepared with the help of previous published maps (Mahajan and Virdi, 2000) and the land cover and road buffer maps were extracted with help of Google earth imagery. All the prepared thematic maps were rasterized at grid size of 30 × 30 with total pixel count for the study area 44,165 for using in the GIS based modeling methods (AHP and FR). In the analytical hierarchy process (AHP) method field survey based judgments and the data from previous literature have helped in assigning weightage (heuristic) to the causal factors and the factor classes whereas in the frequency ratio (FR) method, the ratio of landslide percentage in a factor class and the percentage area of that factor class gave the weightages (statistical). Both the modeling methods (analytical hierarchy process and frequency ratio) have resulted in landslide susceptibility index (LSI) maps which were reclassed using fivefold classification for zoning the landslide prone area which is very low susceptibility (VLS), low susceptibility (LS), medium susceptibility (MS), high susceptibility (HS) and very high susceptibility (VHS). Both the landslide susceptibility zonation maps (LSZM) were validated using the landslide density distribution method and the prediction curve success rates. The evaluations of the resulting landslide susceptibility maps are based on spatial area distribution match between the susceptibility classes for which the error matrix method has been used. The evaluations represent the concordance and the disagreement of class area distribution from the use of heuristic judgements and the objective datasets.

Fig. 3
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Landslide Inventory shown in the hill shade map of the study area prepared using the DEM data

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Analytical hierarchy process (AHP)

Analytical hierarchy process is the decision making for a complex problem by arranging the elements of that problem in a hierarchy. It is a semi-qualitative process in which the weightages to the elements are assigned based on the expert’s judgment and the weightage values vary from 1 to 9 (Saaty, 1980, Saaty and Vargas 2001, Saaty, 2005). The standard scale for using AHP method has been given in Table 1, according to which factor classes and the factors are assigned rating with respect to each other. Value 1 is assigned to the class with least influence and value 9 is assigned to the class with maximum influence. After the weightage assignment the factor maps are reclassed and integrated in GIS.

Table 1 Scale for Pairwise comparison
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For checking the consistency of the comparison matrix prepared by rating factors and factor classes against one another, consistency ratio (CR) is used and the CR value below 0. 1 is considered acceptable (Ayalew et al. 2004).

$$ \mathrm{CR}=\frac{\mathrm{CI}}{\mathrm{RI}} $$
(1)

Where CI is the consistency index calculated as:

$$ \mathrm{CI}=\Lambda\ \max -\mathrm{n}/\mathrm{n}-1 $$
(2)

Where n is the order of the matrix and Ʌ max is the major value of the matrix.

Random index (RI) is the consistency of the randomly generated pair wise matrix and is dependent on the size of the matrix as given in Table 2.

Table 2 Values of random index based on the size f the matrix
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Frequency ratio (FR)

Frequency ratio modeling is based on correlation of landslides in an area with the natural and anthropogenic causal factors in that area. Mathematically, it is the ratio of the percentage of the factor class (y) and the percentage of the landslides (x) in that class (Lee and Talib, 2005; Pradhan, 2010). The correlation factor for FR i.e. 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 and the factor class and if the FR is < 1 then the correlation is weak. All the thematic maps are reclassed according to the FR values for each factor class and then integrated in GIS for generating the landslide susceptibility index (LSI) map.

Landslide inventory

For the preparation of landslide inventory field surveys have been carried out to demarcate the GPS location and the nature of landslides. The vector points of the noted locations were verified using the Google earth imagery and then imported in GIS for applying the heuristic and statistical models. The inventory data was split into training (75%) and testing (25%) groups as shown in Fig. 3 for using in modeling and validation phase respectively. Thirty nine landslides with varying size were demarcated out of which the largest landslide covers an area of 0.103 km2. In total all the landslides cover 1.1 km2 which is 2.7% of the total study area (39.7 km2) and the 75% training data of the total inventory landslide area covers 0.81 km2. Table 3 shows the location and the type of lithology the landslides belong to. Most of the mapped landslides have got activated in the monsoon season (July to September) in form of debris flow mostly. Some of the landslides show mudflow or earth flow type of mass movement which is due to low strength of material and water logging during the monsoons. Figure 4 shows some of the past landslides of the Dharamshala that have caused notable destruction.

Table 3 Landslide Inventory Locations
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Fig. 4
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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

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There exists no set rules for considering the trigger factors in landslide susceptibility mapping, rather the study area characteristics and the data availability guide the choice of thematic layers to be used (Ayalew and Yamagishi, 2005). Based on the study area characteristics, eight parameters discussed below have been considered as the major causal factors for landslides in the Dharamshala region and their thematic maps as shown in Fig. 5 have been prepared at grid size of 30 × 30 with pixel count of 44,165 in each map for modeling in GIS.

  1. 1.

    Distance from drainage (Drainage buffer): The Dharamshala region has a dense drainage network as shown in the location map (Fig. 1) and some of the landslides mapped during the field survey were found in the vicinity of local drainages. To find the distribution of landslides with respect to the drainages flowing in the study area, a drainage buffer map or distance from the drainage map was prepared at proximity of 100 m, 500 m and 1000 m.

  2. 2.

    Distance from fault (Fault buffer): The emplacement of faults in the study area has been found affecting the slope stability as many landslides were found near to the major faults in this region. To find the effect of faults on the mass movement activity a fault buffer map was prepared with three classes showing the proximity of 1000 m, 2000 m and 3000 m.

  3. 3.

    Distance from road (Road buffer): In hilly areas slope excavation for road widening is a common practice which greatly influences the slope stability and similar has been found for the present study area where landslides associated with the slope excavation are common. Considering it one of the main causal factors a road buffer map was prepared with buffer zones of 200 m, 800 m, 1500 m and 2500 m.

  4. 4.

    Lithology: Lithology of an area is closely related to the landslide occurrence as the strength of the emplaced lithology influences the slope stability. In Dharamshala region the lithology is grouped in to four classes which are Dharamsala Group (sandstone, claystone and mudstone), Siwalik Group (boulder conglomerates), Dharamkot Limestone and Chail formation (Schist, Quartzite and Gneissic rocks). According to the landslide inventory data all the landslides are located in the weak lithology of Dharamsala Group of rocks.

  5. 5.

    Soil: This parameter includes the overlying cover on the lithology which has a varying thickness in the present study area and has been grouped into three classes: debris, clay soil and compact alluvial deposits.

  6. 6.

    Land cover: The study area has been divided into four classes: forest cover, settlement on low to moderate slopes, sparsely vegetated area and settlement on steep slopes.

  7. 7.

    Slope angle: The slope map was extracted from the DEM (30 m resolution) and classified into five classes: 0° - 5°, 6° - 15°, 16° - 25°, 26° - 35° and ≥ 35°. These classes represent the slope inclinations throughout the Dharamshala area.

  8. 8.

    Aspect: After the slope extraction slope aspects were extracted using GIS tool and was grouped into nine classes: flat (− 1), N (0° – 22.5° and 337.5° - 360°), NE (22.5° – 67.5°), E (67.5° -112.5°), SE (112.5° – 157.5°), S (157.5° – 202.5°), SW (202.5° – 247.5°), W (247.5° – 292.5°) and NW (292.5° – 337.5°).

Fig. 5
figure 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

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As described in the section 2.1 and 2.2 analytical hierarchy process (AHP) and the frequency ratio (FR) methods were applied on the causal factors and the factor classes for assigning weightages of influence and the frequency ratio for finding correlation with the prevailing landslide conditions of the study area. The results have been presented in Table 4 (AHP) and Table 5 (FR) of section 3 respectively.

Table 4 Comparison matrix of factor classes and the factors based on analytical hierarchy process (AHP)
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Table 5 Frequency ratio values for the factor classes
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Results and discussion

In order to combine all the factor maps reclassed with their weightage values using AHP and frequency ratio (FR) values, the map algebra tool was used which resulted the two-landslide susceptibility index (LSI) maps from both the models. The results of AHP comparison matrix in the Table 4 show that maximum factor weightage results from lithology and soil i.e. 0.35 and 0.25 respectively followed by the weightages of land cover (0.14), drainage (0.07) and slope (0.06) whereas factors such as road, fault and aspect show a little influence on the landslide occurrences. Figure 6b shows the landslide susceptibility zonation (LSZ) map resulted from the heuristic analytical hierarchy process (AHP) method which has been classified using fivefold classification: very low, low, medium, high and very high. Table 5 showing the frequency ratios of the factor classes indicates a strong correlation of Dharamsala Group of rock (FR value is 1.28) with the landslides which also agrees with the results from the AHP method where in the lithology factor the maximum weightage has been given to the Dharamsala Group i.e. 0.71. Among the land cover classes, the settlements on steep angled slopes have the maximum FR value 6.51 indicating major concentration of landslide sites in this class as 32.7% of the landslide area alone falls in this class which covers only 5.04% of the total map area, which indicates an impact of anthropogenic activities on and near steep sloping areas. Among the classes for the slope factor, classes with 16° - 25° and 26° - 35° slope angles (moderate and steep slopes) show maximum FR values 1.48 and 1.35 respectively and collectively include more than 60% of the landslide area. Among the aspects the north-east class with 18.84% landslide area and the north aspect with 8.89% landslide area show maximum FR values 1.34 and 1.10 which indicates more exploitation of north facing slopes or high subsurface moisture conditions due to less sun exposure of northern slopes which makes them unstable. Nonetheless the south facing slopes (SW aspect with 14.76% landslide area) have also shown a high correlation FR value but the maximum FR values for northern slopes are an interesting parameter showing high anthropogenic interference. The debris soil hosts all the inventory landslides (100%) and gives the maximum correlation value 1.33 which is indicative of a shallow nature of maximum mass movements here on the steep slopes. The debris layer composes the weathered lithology from the Dharamsala Group (mudstone, sandstone, claystone) and the overlying fluvial deposits. The drainages have shown a higher correlation (FR = 1.01) at 500 m proximity with 62.96% landslide area whereas for the faults the proximity of 1000 m with 80.17% landslide area and 2000 m seem critical in term of FR values 1.16 and 0.75 respectively. The road buffer factor shows high landslide activity associated within 200 m proximity (FR = 1.51) with more than 50% of the landslide area, which indicated direct impact of slope excavation for road widening in the hilly areas. The resulting Fig. 6a shows landslide susceptibility zonation (LSZ) map from the statistical frequency ratio (FR) method with classes: very low susceptibility (VLS), low susceptibility (LS), medium susceptibility (MS), high susceptibility (HS) and very high susceptibility (VHS). Therefore, a five-fold classification scheme was followed based on natural break classifier option in GIS, which maximizes the variance between the susceptibility classes and represents a clear trend of class index value distribution. The classifications of resulting landslide susceptibility index maps were carried out in such a way so that 20% of the LSZ map area using AHP and FR includes 97% and 76% of the landslides respectively. Table 6 shows the distribution of landslides in various susceptibility classes from heuristic and the statistical methods applied, which shows 18.48 km2 area in high susceptibility class and 4.31 km2 area in very high susceptibility class using AHP method whereas using the statistical method (FR) 9.3 km2 and 13.07 km2 falls under high and very high susceptibility class respectively.

Fig. 6
figure 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

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Table 6 Shows the landslide area along with the landslide density distribution in the susceptibility classes of LSZ maps
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Comparative assessments of LSZ maps from AHP and FR

For checking the reliability of the LSZ maps and comparing their performance for future landslide prediction spatially, various techniques have been proposed like agreed area analysis, prediction rate curve, landslide density distribution etc. (Kayastha et al. 2013; Gupta et al. 2008). In the present study landslide density in the susceptibility zones, prediction rate curves and the error matrix method have been used for assessment and the evaluation of LSZ maps (heuristic and statistical) with respect to each other. Table 6 shows the landslide density distribution among the susceptibility classes which was computed using the ratio of the landslide area in a susceptibility class to the area of that susceptibility class. The density should increase from the low to very high susceptibility class (Gupta et al. 2008) which is true for the present study. In case of AHP method the high (HS) and very high susceptibility (VHS) class have density value 0.022 and 0.095 respectively whereas, for the FR method the HS and VHS class have density value 0.004 and 0.059 respectively. Therefore, the susceptibility of various zones in both the maps matched the inventory data distribution noted from the field studies and also both the LSZ maps show a reliable similarity with varying values of landslide density distribution.

The validation of the susceptibility maps from AHP and FR technique was carried out using the prediction rate curve which computed the cumulative percentage of landslide occurrences (testing data) in both susceptibility zonation maps (Sarkar et al. 2013) which is shown in Fig. 7. The prediction curves were analyzed using area under curve (AUC) values which indicate the model fitness for landslide prediction in which value below 0.5 refers low accuracy level whereas value from 0.5 to 1 refers higher accuracy of the models used. In this study both the model heuristic (AHP) and statistical (FR) have shown AUC value above 0.5, where AHP method gave 76.77% (0.76) AUC and the FR method gave 73.38% (0.73) AUC. These results show that both the methods have given a good prediction rate for estimating the future landslide probabilities spatially.

Fig. 7
figure 7

Graph representing prediction rate curves of statistical model FR (Red trend line) and AHP (Blue trend line) for interpretation of model fitness for landslide susceptibility mapping and their respective AUC values

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Evaluation of susceptibility zonation maps

Both comparison method: landslide density distribution and prediction rate curve have shown that AHP and FR techniques gave interpretations on a positive side but there exists difference in the results of both the LSZ maps generated i.e. the area of each susceptibility class varies in the maps from AHP and FR method. The spatial differences between the susceptibility classes can help to evaluate the LSMs and can state that, how the choices of subjective and objective judgements in heuristic and statistical methods respectively influence our results. To analyze the spatial difference among landslide susceptibility classes an error matrix method was used (Gupta et al., 2008; Kayastha et al. 2012) which is presented in Table 7. Using the combination of AHP-FR maps error matrix was tabulated showing a high degree match between areas of VLS, LS and MS zones of both the LSZ maps which indicates a similarity of 16.95 km2 area in total which constitutes 42.6% of the total map areas. In case of high susceptibility zone (HS) and very high susceptibility (VHS) zone the difference in the areas is more i.e. for AHP 4.32 km2 area is covered in VHS zone whereas for FR map 13.07 km2 area is covered in VHS zone but, in total more than 55% of both the map’s susceptibility classes show spatial agreement. These differences in the areas of HS and VHS class can be due to difference of the methods used for susceptibility mapping where in the AHP method subjective judgment approach was used for determining the factor weightages whereas in the FR method a bivariate statistical approach was used to compute weights of each class separately. This evaluation has also helped to analyze the agreement of area distribution (pixels or km2) in the resulting LSMs which ascertains the consistency of causal factors used in the study whereas, the disagreement of area distribution refers to the difference of techniques used. Nonetheless, 100% of the observed landslide area falls in the high susceptibility and very high susceptibility classes which shows good prediction rate of both LSZ maps.

Table 7 Shows the error matrix for computing spatially agreed area between the landslide susceptibility classes in AHP and FR LSZ maps
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Conclusions

The findings in this study point out the following conclusions:

  1. 1)

    The work shows a comparative study of GIS based heuristic and statistical models for landslide susceptibility zonation of Dharamshala region of Himachal Pradesh, India. The lithology and the land cover factors have shown maximum contribution toward landslide occurrence based on the computed weightage values using AHP and FR models. The anthropogenic interferences in this hilly terrain have caused huge impact on the slopes and the condition is worsened as the internal properties of the lithology and the overlying debris material are weak due to which instability of slopes is triggered. Maximum landslide locations were mapped in close proximity of the roads and the local drainages.

  2. 2)

    The landslide susceptibility zonation maps from both the methods have been classified into five zones: very low susceptibility (VLS), low susceptibility (LS), medium susceptibility (MS), high susceptibility (HS) and very high susceptibility (VHS). Both the LSZ maps show a good model fitness for predicting future landslide locations based on prediction rate curve method. Landslide density distribution increases from low to very high susceptibility class of both the LSZ maps which represents an agreement with the field conditions of the study area. Such results have inferred a statistical similarity between both the resultant susceptibility maps.

  3. 3)

    The LSMs prepared have been evaluated to check the consistency of area distribution among the susceptibility classes from AHP and FR technique. The evaluation of the susceptibility maps was based on the error matrix method which resulted into differences and the similarities of area (km2) assigned to each susceptibility zone. The results have shown a good consistency in the spatial area distribution in very low, low and medium susceptibility classes of LSZ maps which count for 42.6% of the susceptibility map areas. For the high and very high susceptibility classes the spatial area distribution in both the LSZ maps varies to some extent but this difference factor is hindered as both these classes HS and VHS include 100% landslide affected area in each resulting LSZ map. The spatial difference of susceptibility classes can be attributed to the variance of procedure [subjective (AHP) and objective (FR)] in weighting the factors and their classes whereas, the spatial similarity of the susceptibility zones may have occurred due to the use of similar causal factors and the landslide inventory data for both the modeling methods.

  4. 4)

    The results from the final map evaluations indicate that the 100% landslide data falls in the high susceptibility (HS) and very high susceptibility (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.