LANDSLIDE SUSCEPTIBILITY MAPPING USING GIS BASED PROBABILISTIC APPROACHES IN KALIMPONG AND DARJEELING, WEST BENGAL, INDIA

Abstract

In West Bengal, India's Darjeeling Kalimpong area, landslides are a serious concern. With the use of four GIS-based techniques, including the Shannon Entropy (SE), Statistical Index Method (SIM), and Weight-of-Evidence (WoE), this study attempts to create an extensive map of landslip susceptibility. These techniques were chosen because they are good at managing huge datasets, tolerating various factors, and giving reliable estimates of landslip vulnerability. The research area was split into two parts: the first was for training the models, and the second was for model validation. A total of 13 conditioning factors were chosen and examined for their impact on the likelihood of landslides, including elevation, slope, aspect, curvature, distance from rivers, roads, and lineaments, lithology, land use/cover, stream power index, topographic wetness index, rainfall, and geology. Each strategy was put into practise, and the resulting maps of landslip susceptibility were compared and assessed. The analysis's findings demonstrated that all four models were useful for estimating the likelihood of landslides, with the SI, and WoE models performing somewhat better than the SE model. The Receiver Operating Characteristic (ROC) curve was used to evaluate the models' accuracy, and it revealed that the SI, SE, and WoE models had AUC values of 0.826, 0.77, and 0.825, respectively. The landslip inventory data was used to evaluate and validate the landslip susceptibility maps produced by the four models. The comparison revealed that for predicting landslip susceptibility, the SI and WoE models performed better than the SE model. This work provides useful data for land use planning and disaster management in the study region and shows the efficiency of GIS-based models in landslip susceptibility mapping.