Landslide Susceptibility Mapping through Weightages Derived from Statistical Information Value Model

Volume 4 Issue 4 December - February 2018

Research Paper

Landslide Susceptibility Mapping through Weightages Derived from Statistical Information Value Model

A. Kumar *, R. Kalita**, A. Sharma ***, V. Ranga****, J. S. Rawat *****

*-***** Centre of Excellence for NRDMS in Uttarakhand, Department of Geography, Kumaun University, SSJ Campus, Almora,

Kumar, A., Kalita, R., Sharma, A., Ranga, V., and Rawat, J. S. (2018). Landslide Susceptibility Mapping through Weightages Derived from Statistical Information Value Model. i-manager’s Journal on Pattern Recognition, 4(4), 10-20. https://doi.org/10.26634/jpr.4.4.14129

Abstract

Landslides pose a great risk to life and property, therefore landslide susceptibility assessment is of vital importance, especially, in the hilly terrain. The key objective of this study is to generate a landslide susceptibility map through integrating weightages of different categories of the landslide causative factors derived from Statistical Information Value Model (SIVM) under Geographic Information Systems (GIS) environment. Several causative factors, such as slope, slope aspect, geology, drainage proximity, structural feature proximity, Landu Use/ Land Cover (LU/LC), NDVI, curvature, topographic wetness index, stream power index, road proximity, and relative relief were identified in this study area resulting in slope failures to a great extent. The existing landslides were mapped using remotely sensed data and field survey which were then divided into 70% (model training, i.e. calibration) and 30% (model testing, i.e. validation) data sets. Finally, the Landslide susceptibility map derived from statistical information value model has been divided into five equal classes, namely Very Low, Low, Moderate, High, and Very High. The accuracy of the model was evaluated using Receiver Operation Characteristic (ROC) curve, which resulted in 0.86 areas under curve. The area under curve figure reflects that the prediction accuracy of model is 86% and the results obtained will be useful for the policymakers in the study area for the generation of key plans and decision-making tasks.