Flood susceptibility mapping using ensemble machine learning models and SHAP analysis in the Shebelle River Basin, Somalia

The Shebelle River Basin in Somalia is perennially threatened by devastating floods, yet it remains profoundly understudied, with existing conventional models, incomplete variable selection, and a lack of model interpretability. This study addresses these gaps by proposing an Explainable Stacking Ensemble Model designed for high-precision and transparent flood risk assessment. Our sophisticated stacking architecture integrates five diverse base models, XGBoost, LightGBM, and CatBoost, whose predictions are synthesised by a CatBoost meta-classifier to achieve superior predictive performance. The model was trained on a comprehensive set of seventeen conditioning factors using a balanced dataset of 4,154 locations. Our proposed model demonstrated exceptional performance, achieving (Accuracy: 91.26%, F1-Score: 91.36%, ROC-AUC: 0.9723) and outperforming Gradient Boosting Machines, Extra Trees, XGBoost, LightGBM, and CatBoost. Crucially, to dismantle the ‘black-box’ nature of complex ensembles, we embedded SHAP (SHapley Additive exPlanations) analysis directly into the framework. This provides unparalleled interpretability, revealing Topographic Wetness Index (TWI), elevation, rainfall, and stream order as the most influential factors driving flood susceptibility. The resulting high-resolution risk map accurately identifies high-risk zones along the middle and lower reaches of the Shebelle River, aligning with historical events.