Landslide Dam Susceptibility Mapping in the Indian Himalayas: A Random Forest Approach with Cross-Catchment Validation

Landslide dams represent a major geomorphic hazard in the seismically active Himalayan belt, where temporary river blockages can lead to catastrophic outburst floods that impact downstream communities and infrastructure. Despite their importance, landslide dam susceptibility remains underexplored compared to conventional landslide hazard assessment. This study addresses this gap by developing a machine learning-based susceptibility model specifically targeting landslide dam formation, with the evaluation of spatial transferability between adjacent river basins. The following fifteen conditioning variables was compiled from diverse geospatial datasets: slope, aspect, elevation, plan curvature, relative relief, Topographic Wetness Index (TWI), distance to stream, distance to fault, distance to lineament, lithology, geomorphology, land use land cover (LULC), and median values of Normalised Difference Vegetation Index (NDVI), Normalised Difference Moisture Index (NDMI), and Normalised Difference Water Index (NDWI). A Random Forest (RF) classifier was implemented and trained exclusively on the Alaknanda basin and then applied to the neighbouring Bhagirathi basin for external validation, ensuring strict spatial separation between the training and test domains. The RF model achieved strong internal performance in the Alaknanda basin, and external validation in the Bhagirathi basin demonstrated robust transferability, with only modest performance degradation. Feature importance analysis revealed that elevation, NDMI, aspect and relative relief were the primary controls on dam formation. Susceptibility maps identified high-risk zones concentrated along deeply incised river valley segments, fault intersections, and areas underlain by high-grade metamorphic rocks. This susceptibility map may provide actionable information for disaster risk assessment, infrastructure planning, and the development of early warning systems in the Alaknanda–Bhagirathi river system and similar mountain regions worldwide.