Developing an explainable and interpretable machine learning model for flood susceptibility mapping

This study evaluates flood susceptibility in the Fez-Meknes region of Morocco by comparing the performance of five machine learning (ML) models using 14 environmental variables.The selected models, including random forest (RF), support vector machine (SVM), K-Nearest neighbor (KNN), recursive partitioning and regression trees (RPART), and logistic regression (LR), were assessed for prediction accuracy and enhanced with partial dependence plots (PDP) and local interpretable model-agnostic explanations (LIME) to increase interpretability.Results indicate that the RF model outperforms other models, achieving a high prediction accuracy with an AUC of 96%, low mean absolute error (MAE) of 0.26, and root mean squared error (RMSE) of 0.31, along with strong Nash Sutcliffe efficiency (NSE) and correlation coefficient (R).Through PDP and LIME, the primary factors influencing flood susceptibility were identified as proximity to rivers, drainage density, slope, normalized difference vegetation index (NDVI), terrain roughness index (TRI), and land use and land cover (LULC).These findings highlight the potential of interpretable ML models to enhance flood risk assessment, providing valuable insights for urban planning and flood mitigation strategies in vulnerable regions.