Artificial Intelligence and Machine Learning Driven Landslide Analysis: Scar Segmentation, Susceptibility and Sensitivity Analysis using Naive Bayes and K-nearest neighbor in Nainital District, Uttarakhand

Landslides represent a recurrent hazard in mountainous regions, notably in the geologically fragile Nainital district of Uttarakhand, India.This study develops a data-driven landslide susceptibility model using k-Nearest Neighbors (KNN) and Naive Bayes (NB) classifiers, integrating 273 inventoried landslide scars and eight geo-environmental conditioning factors.The dataset was partitioned into 70% training and 30% testing subsets.Model performance, evaluated using area under the ROC curve (AUC), yielded predictive accuracies of 0.79 (KNN) and 0.81 (NB).The analysis revealed that 33.92% of the study area falls within high to very high susceptibility zones, concentrated along south-and southwest-facing slopes (25-60), elevations of 900-1800 m, and areas proximal to roads and drainage.Stream Power Index and Topographic Wetness Index emerged as dominant contributors.The findings affirm the applicability of AI-based models for reliable landslide prediction and offer strategic insights for risk-informed planning and disaster mitigation in Himalayan terrain.