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, curvature, aspect, distance to river (DTRiver), Distance to road (DTRoad), normalized difference vegetation index, slope, curve number, topographic wetness index (TWI) and rainfall. RF was the best performing model with an area under curve (AUC) of 0.968 and an F1-score of 0.92, demonstrating its high performance and robustness in flood susceptibility analysis. In addition, RF, combined with SHAP, provided both robust and interpretable results. The study found that DTRiver, TWI, DTRoad, and slope had the highest influence on model predictions, while curve number had the least. RF classified the area into five flood susceptibility classes: very high (6%), high (9.6%), moderate (15.1%), low (26.6%), and very low (42.7%), accurately reflecting environmental and geo-topographic conditions. Based on these findings, mitigation measures can be designed to reduce flood risk in the Nyabarongo catchment. Additionally, the models have potential for application across Rwanda to improve flood susceptibility management and could be adapted for use globally.
Nzabonantuma et al. (Tue,) studied this question.
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