Probabilistic framework for enhanced Landslide Susceptibility Mapping for Rainfall-Induced Landslides

In India, landslides are one of the severe disasters with the highest fatality rate. Over the past few years, due to the heavy and prolonged rainfall events, there has been a surge in landslides in the Northwestern Himalayan region. Himachal Pradesh has faced an economic loss of $60 million alone in the 2021 monsoon season, with more than 200 casualties, followed by severe damage caused in the 2023 and 2025 monsoons. To mitigate the risk, landslide susceptibility mapping (LSM) has emerged as a fundamental step that can help in formulating policies for high-risk areas. Statistical methods, deterministic approaches and remote sensing techniques have been extensively employed by various researchers to forecast landslides. This paper introduces a novel LSM framework which utilises both natural and anthropogenic conditioning factors to develop pixel-based site-specific susceptibility. The natural parameters include topography (elevation, slope, aspect), geomorphology, distance to streams, water table depth. Anthropogenic factors include Normalized Difference Vegetation Index (NDVI) change, distance from roads. This study integrates the quantitative methods along with the qualitative expert knowledge to develop enhanced susceptibility maps for the 3 landslide events that occurred in the months of July and August 2023 in Mandi district. To overcome the simplicity and uncertainty of parameters probability of failure is utilized to reframe the susceptibility. The buffer zone for each landslide is categorized into 3 zones based on risk associated: low risk (green), medium risk (yellow), and high-risk (red) zone. Cross-validation is employed to evaluate the generalization capability of models across the landslide sites, to understand their inter-site transferability. 

 

Keywords: Rainfall induced landslides, Probability of failures, susceptibility mapping, uncertainty analysis