Landslide susceptibility mapping with explainable AI techniques: Evidence from Bavaria, Germany

Abstract. Landslides threaten infrastructure, ecosystems, and human safety, particularly in mountainous regions. Climate change with increasingly intense rainfall, together with growing populations and assets in hazard-prone areas, increases the need for accurate and interpretable landslide susceptibility assessments. This study presents a region-wide landslide susceptibility map modeled for entire Bavaria, Germany, based on more than 11,000 recorded landslide events. Using slope units, which are terrain-based spatial mapping entities following natural drainage lines and ridges, the model captures landslide-prone areas in a more terrain-consistant manner than traditional grid-based approaches. To generate the landslide susceptibility map, we employ a dense neural network architecture. The model is trained on the landslide inventory and a wide range of landslide-influencing factors derived from high-resolution topographic, geological, and land cover data and achieves strong predictive performance (ROC AUC = 0.953, PR AUC = 0.844). Model interpretability is approached using the SHapley Additive exPlanations (SHAP) framework, which provides both global and local insights into the factors influencing landslide susceptibility, revealing a strong predictive influence of geology, soil properties and terrain heterogeneity. The resulting susceptibility map is compared with an existing map, which is based on manual assessments, and shows good performance, particularly for deep-seated landslides. However, evaluation using newly recorded landslides reveals limitations in the model's generalizability. Many newly recorded events occur in regions that were underrepresented in the original inventory and are therefore wrongly assigned low susceptibility values. This demonstrates how spatial incompleteness and selection bias in landslide inventories directly propagate into susceptibility maps, leading to systematic underestimation of hazard. Overall, this study highlights that while explainable machine learning enables robust and more interpretable regional susceptibility mapping, the quality and spatial completeness of landslide inventories are critical for reliable hazard assessment and mitigation.