Modeling Flood Risk in Kalaa Sraghna Region in Morocco Using Explainable Artificial Intelligence Techniques

Abstract

Abstract. Predicting flood risk is a complex phenomenon. Several factors influence flood behavior generation and intensity such as intricate interactions between hydrological dynamics, meteorological variability, the overarching influence of climate change and land-use changes. This study explores flood risk within the watershed of Tassaout River located in the central region of Morocco. Three advanced machine learning algorithms were chosen to evaluate flood risk. These algorithms are Multi-Layer Perceptron Artificial Neural Networks (MLP-ANN), Random Forest (RF) and Support Vector Machine (SVM). The models are trained based on 11 different factors derived from remote sensing data. From ALOS digital elevation model, 8 factors are developed: Elevation, Slope, Aspect, Plan Curvature, Profile Curvature, Stream Power Index (SPI), Topographical Wetness Index (TWI), and Surface Roughness. In addition, from Landsat 9 imagery, three flood susceptibility factors are extracted: Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Land Surface Temperature (LST). The predictive performance of each model was assessed using standard classification metrics: accuracy, recall, and F1-score. Results indicate that the RF model performed the best with an accuracy of 100%, SVM algorithm achieved good performance, attaining 68% in accuracy and more than 80% in f1-score. However, the ANN model underperformed compared to the other algorithms, with an accuracy of only 59% in accuracy and 70% in f1-score highlighting its limitations in capturing the decision boundaries within the current data configuration. Furthermore, the Shapley Additive exPlanations model (SHAP) was used to enhance the transparency and interpretability of the modelling results.