Landslide Hazard, Vulnerability, and Risk Analysis (HVRA) Using Machine Learning and AI: A Case Study of the Darma Valley, Kumaun Himalaya, India

Landslides have become one of the most destructive geological hazards in the Himalayan region, exhibiting a significant increase in both occurrence and intensity in recent decades. This increasing trend poses serious threats to human life, infrastructure, and essential public assets, underscoring the need for comprehensive risk evaluation in these highly vulnerable mountainous terrains. The present study offers an extensive assessment of landslide hazard, vulnerability, and associated risk in the Darma Valley of the Kumaun Himalaya, India. Landslide susceptibility was modelled using a Multilayer Perceptron (MLP) neural network, and the model’s predictive performance was validated through ROC–AUC analysis. Vulnerability was quantified by integrating land-use/land-cover categories with their respective economic valuations. Furthermore, rainfall and seismic intensity maps were combined with the susceptibility outputs to derive a detailed landslide hazard map. The results indicate that roads are the most vulnerable elements, followed by settlements and dam infrastructures, largely due to their substantial reconstruction costs and higher exposure levels. The final risk map, produced by integrating hazard and vulnerability layers, reveals that approximately 9% of the study area falls within high to very high risk zones, 22% within moderate risk, 26% within low risk, and 43% within very low risk zones. These findings offer essential guidance for promoting sustainable development and supporting land-use planning that accounts for environmental risks. They also contribute to more informed and effective decision-making aimed at strengthening the resilience of the fragile and sensitive Himalayan landscape.