Comparative landslide susceptibility assessment using statistical information value and index of entropy model in Bhanupali-Beri region, Himachal Pradesh, India

Abstract

Landslide is a complex natural hazard that sometimes causes disaster resulting in loss of life, assets and infrastructure, especially in the Himalayas. Recent studies suggest that for effective mitigation and resilience through proper planning and policymaking, it is equally important to justify and select a suitable scientific technique that most appropriately addresses the salient causes of a landslide in any area. The principal objective of this study is to carry out a comparative assessment between two contemporary statistical techniques, i.e., the statistical information value (SIV) and index of entropy (IOE), to find out the effectiveness of the two said methods in landslide susceptibility mapping in Bhanupali-Beri region. During the analysis, the higher-resolution satellite images, i.e., World view-2 image of 2017 and Landsat-8 OLI image of 2018, have been used for delineation of various triggering parameters used for landslide susceptibility. The contemporary GIS technique integrated with the remote sensing applications was distinct in preparing the prominent landslide conditioning factor layers such as slope, slope aspect, thrust and fault proximity, geomorphology, landuse–landcover, stream power index, topographic wetness index, geology, roads proximity, lineament density and past landslide inventory. The final assessment was performed using GIS software through raster re-sampling, and the values derived for each conditioning factors were combined using defined SIV and IOE equations. The study area was categorized into five distinct landslide susceptible zones (very low, low, moderate, high and very high) using the Jenk’s Natural Breaks algorithm. Index of entropy model has given better results compared to SIV. The utmost vital factors triggering landslide (estimated for entropy values) in the area are landuse–landcover with barren land and sparse vegetation followed by TWI, lineament density, geomorphology, and slope.