Interpretable Landslide Hazard Analysis in the Western Ghats of Maharashtra, India: A Hybrid Machine Learning and Statistical Approach

ABSTRACT Landslide hazard mapping is critical for disaster risk reduction and resilient planning in vulnerable regions. Whilst the existing methods for Landslide hazard mapping (LHM) provide good predictability, they still lack in analysing the impact of parameters on the hazard risk. Although Explainable AI (XAI) application enhances this capability, its use in LHM analysis is still limited. This study presents an interpretable framework for evaluating landslide hazards in Raigad district, Maharashtra, that integrates machine learning models, Random Forest (RF) and Support Vector Machine (SVM) with statistical techniques, Frequency Ratio (FR) and Shannon Entropy (SE). Sixteen conditioning factors, selected through multicollinearity screening and feature‐selection methods and spatially validated inventory of 174 datasets of landslides and non‐landslides each, were used for analysis. Model performance was assessed using evaluation metrics. The RF model achieved the highest AUC‐ROC of 0.90, followed by SVM (0.81), SE (0.80), and FR (0.79). To identify critical parameters affecting landslide vulnerability, XAI methods such as SHapley Additive exPlanations (SHAP) and partial dependence plot analysis were applied. Slope angle emerged as the most dominant predictor of landslide risk. The generated hazard maps can offer more useful insights into the categorisation of land based on the degree of landslide risk. These maps can be used in development planning to help with infrastructure design, land‐use zoning, and prioritisation of high‐risk locations for mitigation.