ABSTRACT
Landslides are one of the major threats in Sikkim’s Himalayan region. The current study’s goal is to determine the probability of landslides in the Rorachu watershed of the East Sikkim Himalaya using the Bivariate Statistical Index (BSI), Index of Entropy (IOE), and weight of evidence (WOE) models. The National Highway (NH 31A) road in the East Sikkim Himalaya is prone to numerous landslides. In the beginning, a field survey, aerial pictures, and records of previous landslides were used to create the landslide inventory map. Using geographic information systems software, 147 landslide locations were mapped with 30% used for model validation and 70% used for model training. The 13 landslide causative factors were used in this modeling. These factors were used to create the Landslide Susceptibility Index (LSI) and landslide risk assessment (LRA) maps. The LSI maps were validated using the receive operation curve, landslide density, and success rate curve. The results show that the WOE model-generated LSI map has the highest prediction accuracy, with an area under the curve (AUC) value of 0.858. The BSI model and the IOE model are close behind with AUC values of 0.858 and 0.687, respectively. The elements-at-risk (road and settlement) identified by the LRA revealed that BSI (40.04%), IOE (38.22%), and WOE (39.78%) had predicted the highest risk for the NH 31A road and the highest risk for settlement areas, respectively. With the aid of these maps, land-use planning strategies can be developed, aiding mitigation efforts and reducing the risk of fatalities.
