Machine learning-based optimization of flood susceptibility mapping in semi-arid zone
Hassan Ait Naceur, Brahim Igmoullan, Mustapha Namous
Abstract
Flooding is a significant hazard, resulting in the devastation of infrastructure and loss of life. This research seeks to forecast flood susceptibility in the Oued Lakhdar watershed in the Moroccan High Atlas by combining the Frequency Ratio (FR) statistical model with three machine learning algorithms: Random Forest (RF), Decision Tree (DT), and Support Vector Machine (SVM). High-resolution Google Earth imagery and field data were utilized to delineate 154 flooding occurrences, resulting in a thorough inventory map. Moreover, multiple data sources were analyzed to create thematic maps of various conditioning elements, resulting in the selection of 11 pertinent factors following a multicollinearity assessment. The findings indicate that the FR-SVM model had exceptional performance, with an area under the curve (AUC) of 93.32%. The FR-RF and FR-DT models exhibited commendable performance, with AUCs of 91.64% and 89.41%, respectively. Moreover, the FR-SVM model achieved a sensitivity of 95.88% and a specificity of 97.64%, illustrating its ability to minimize false positives while reliably identifying at-risk regions. The generated maps delineate regions with a heightened risk of flooding, especially in the northern section of the basin and adjacent to rivers with gentle slopes. These findings offer a solid foundation for decision-makers to enhance flood prevention and mitigation efforts in this susceptible area. Moreover, the outstanding efficacy of the developed FR-SVM model underscores its validity in analogous conditions in semi-arid regions.