Extreme gradient boosting with Shapley Additive Explanations for landslide susceptibility at slope unit and hydrological response unit scales

• Hydrological Response Units (HRU) was used as mapping unit and compared with the result of Slope Units (SU). • Employed Extreme Gradient Boosting (XGB) combined Hyper parameter tuning with Shapley Additive Explanations (SHAP) algorithm. • HRUs performing better than SUs across all evaluated metrics for land susceptibility mapping. This study provides an in-depth comparative evaluation of landslide susceptibility using two distinct spatial units: and slope units (SUs) and hydrological response units (HRUs), within Goesan County, South Korea. Leveraging the capabilities of the extreme gradient boosting (XGB) algorithm combined with Shapley Additive Explanations (SHAP), this work assesses the precision and clarity with which each unit predicts areas vulnerable to landslides. SUs focus on the geomorphological features like ridges and valleys, focusing on slope stability and landslide triggers. Conversely, HRUs are established based on a variety of hydrological factors, including land cover, soil type and slope gradients, to encapsulate the dynamic water processes of the region. The methodological framework includes the systematic gathering, preparation and analysis of data, ranging from historical landslide occurrences to topographical and environmental variables like elevation, slope angle and land curvature etc. The XGB algorithm used to construct the Landslide Susceptibility Model (LSM) was combined with SHAP for model interpretation and the results were evaluated using Random Cross-validation (RCV) to ensure accuracy and reliability. To ensure optimal model performance, the XGB algorithm’s hyperparameters were tuned using Differential Evolution, considering multicollinearity-free variables. The results show that SU and HRU are effective for LSM, but their effectiveness varies depending on landscape characteristics. The XGB algorithm demonstrates strong predictive power and SHAP enhances model transparency of the influential variables involved. This work underscores the importance of selecting appropriate assessment units tailored to specific landscape characteristics for accurate LSM. The integration of advanced machine learning techniques with interpretative tools offers a robust framework for landslide susceptibility assessment, improving both predictive capabilities and model interpretability. Future research should integrate broader data sets and explore hybrid analytical models to strengthen the generalizability of these findings across varied geographical settings.