From Black-Box Predictions to Trustworthy Landslide Susceptibility Mapping Using Explainable AI

Landslides are one of the most destructive natural hazards, causing significant loss of life, extensive damage to infrastructure, and long-term disruption to socioeconomic development, particularly in rapidly urbanizing regions. Consequently, accurate landslide susceptibility mapping is a critical tool for effective hazard assessment and risk management. Although the extensive use of machine learning algorithms for landslide susceptibility mapping, the black-box nature of the models often limits the acceptance of model results by decision-makers. This study presents an explainable artificial intelligence framework for landslide susceptibility mapping that integrates SHapley Additive exPlanations (SHAP) with Recursive Feature Elimination (RFE) to optimize ensemble machine learning models. The proposed framework was tested on an arid and rapidly developing region in East Cairo, Egypt. A landslide inventory of more than 180 events was compiled from field surveys and satellite imagery, and fourteen conditioning factors representing topographic, geological, and anthropogenic controls were initially considered. Unlike traditional feature selection approaches that rely mainly on statistical importance, the proposed framework selects predictors based on their physical and geological contribution to slope instability. The results show that SHAP-based feature selection significantly reduces model complexity while maintaining high predictive performance, with only five predictors for Random Forest and nine for XGBoost. Beyond predictive performance, the framework provides clear physical and geological explanations for slope failure processes. SHAP interaction analysis identified two dominant instability mechanisms: human-induced factors within a 200 m buffer around the road cuts, as well as structural instability on slopes with orientations ranging from 225° to 320°, as expected from kinematic conditions for daylighting within the area of study. These findings demonstrate that explainable AI can move beyond black-box prediction by linking machine learning outputs to geological ground truth. Overall, this proposed framework offers a practical and interpretable tool for landslide hazard assessment and sustainable land-use planning, particularly in data-scarce and rapidly developing environments.

Keywords: Explainable AI, SHAP, feature selection, landslide susceptibility, Hazard assessment