Explainable Machine Learning-Based Flood Susceptibility Mapping in Wadi Numan, Makkah, Using GIS, Remote Sensing, and Hydro-Environmental Variables

Flooding is a major natural hazard in arid and semi-arid regions, posing significant risks to infrastructure, property, and public safety. This work developed a machine learning-based framework for flood susceptibility assessment in Wadi Numan, Saudi Arabia, using eight flood conditioning factors: Surface Elevation (SE), Slope, Topographic Wetness Index (TWI), Curve Number (CN), Drainage Density (DD), Normalized Difference Vegetation Index (NDVI), Rainfall Depth (RD), and Terrain Ruggedness Index (TRI). A total of 42 classification models available in the MATLAB Classification Learner application were evaluated and compared alongside Artificial Neural Network (ANN) Pattern Recognition models. Model performance was assessed using accuracy, sensitivity, precision, specificity, F1-score, confusion matrices, and ROC curves. Among the evaluated models, Logistic Regression achieved the best overall performance, with an accuracy of 79.05%, sensitivity of 79.61%, precision of 78.10%, specificity of 78.51%, and an F1-score of 78.85%. SHAP analysis was applied to the best-performing model to quantify the relative importance of the flood conditioning factors. The results identified Surface Elevation (SE) (27.46%) as the most influential factor, followed by Topographic Wetness Index (TWI) (21.52%), Drainage Density (DD) (15.23%), Curve Number (CN) (11.25%), Rainfall Depth (RD) (10.19%), Terrain Ruggedness Index (TRI) (7.58%), NDVI (5.25%), and Slope (1.52%). The final flood susceptibility map was generated using a weighted overlay approach, integrating the selected flood-causing factors according to their derived importance weights. The flood susceptibility map showed that approximately 19.07% of the watershed (128.81 km2) falls within the High and Very High susceptibility classes, while the Moderate class accounts for 37.34% of the area. These results identify the areas requiring greater attention for flood management and mitigation. The developed framework provides useful information for infrastructure planning and sustainable watershed management and can also be applied to other arid and semi-arid watersheds.