Landslides are a persistent hazard in the tectonically active Central Himalaya, frequently affecting roads and settlements. However, quantitative assessments of their spatial drivers have remained limited. This study investigated landslide susceptibility along a 90 km section of the Uttarkashi–Gangotri highway to identify dominant triggering factors and generate a reliable risk map. We applied the AHP–GIS framework, guided by a multi-criteria decision-making approach. Nine thematic parameters, such as slope, geology, lineament density, drainage density, proximity to roads, rainfall, aspect, curvature, and land use/land cover were utilised to quantify their relative influence on slope failure. Results showed that slope (23%), geology (22%), and lineament density (21%) were the most influential factors. Secondary contributions came from drainage density (9%), proximity to roads (8%), and rainfall (>231 mm). The susceptibility map was validated using 105 landslide inventory points, with 64 events (61%) located in very high-risk zones and 31 (30%) in high-risk zones. The model achieved a predictive accuracy of 0.817 based on the Area Under the Curve (AUC) metric. High-risk zones are aligned with steep slopes (30–50°), convex curvatures, and barren land, particularly near infrastructure. These findings provide a scientific tool for hazard mitigation and support disaster risk reduction in similar mountainous regions worldwide, contributing to safer infrastructure development.
Dwivedi et al. (Sun,) studied this question.