Influence of topographic parameters on landslide susceptibility using machine learning: A case study in the municipality of São Sebastião, São Paulo, Brazil.

Understanding the influence of topographic parameters on landslide susceptibility (LSM) is crucial for risk management in regions where landslides are recurrent and potentially catastrophic. Although landslides are often triggered by short-term external forcings such as intense rainfall, the expansion of human settlements onto steep slopes greatly amplifies their impacts, making prediction and mitigation increasingly urgent and challenging.

The Serra do Mar is a mountain chain extending over 1,500 km along the southeastern coast of Brazil, separating the inland plateau from the coastal plain and characterized by rugged relief strongly controlled by geological structures, including faults and steep escarpments. High seasonal rainfall combined with intense weathering makes this region naturally prone to landslides, as dramatically illustrated in February 2023, when extreme rainfall triggered widespread slope failures in the municipality of São Sebastião (São Paulo State), causing severe damage and loss of life.

Despite the importance of such events, traditional landslide susceptibility mapping approaches, largely based on field surveys and geotechnical analyses, are costly and time-consuming. Remote sensing combined with explainable machine learning offers a powerful alternative for large-scale spatial hazard assessment.

This study investigates how different Digital Elevation Model (DEM) resolutions affect predictive landslide susceptibility modeling using machine learning and explainable artificial intelligence (XAI) techniques. A multiscale set of topographic predictors was derived from airborne lidar and Copernicus DEMs. These predictors were integrated with a landslide inventory from the February 2023 event (1,070 mapped scars), which served as the reference dataset for training and spatially validating Random Forest susceptibility models, enabling a direct comparison of how different DEM resolutions reproduce observed landslide patterns. Model interpretability was then assessed using SHAP (Shapley Additive Explanations) to quantify scale effects and the relative contribution of topographic controls on landslide susceptibility.