Comparative evaluation of GIS-based landslide susceptibility mapping using statistical and heuristic approach for Dharamshala region of Kangra Valley, India

Abstract

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. 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.