Comparative assessment of landslide susceptibility using heuristic and statistical models in Chamoli, Uttarakhand, India

Abstract

Landslides pose a recurring threat to human settlements, infrastructure, and ecosystems in the seismically active Chamoli district of Uttarakhand, India. This study presents a comparative landslide susceptibility assessment using three distinct models: Frequency Ratio (FR), Shannon Entropy (SE), and Analytical Hierarchy Process (AHP). Twenty-four geo-environmental and anthropogenic conditioning factors were integrated to develop landslide susceptibility maps (LSMs) tailored to the region’s complex terrain. Multicollinearity analysis was conducted to ensure statistical robustness, and model performance was validated using the Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC) metric. The FR model achieved the highest predictive accuracy (AUC = 0.819), followed by AHP (0.789) and SE (0.594). While FR demonstrated superior data-driven reliability, AHP offered interpretability grounded in expert judgment. Thematic analysis revealed slope, geology, rainfall, proximity to roads, and land use changes as key landslide triggers. Susceptibility zonation showed significant spatial variability across models, with high-risk zones concentrated near road corridors, riverbanks, and deforested slopes. The study underscores the value of methodological triangulation in landslide prediction and recommends FR as a reliable framework for future hazard planning. These findings provide actionable insights for disaster mitigation, infrastructure planning, and sustainable land use management in Himalayan regions.