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Rwanda’s Nyabarongo catchment frequently experiences floods, highlighting the need for effective flood susceptibility analysis and management. This study mapped flood susceptibility in the catchment using the random forest (RF), support vector machine (SVM), eXtreme Gradient Boosting (XGBoost), SHapley Additive exPlanations (SHAP) models, as well as various conditioning factors including elevation
