Machine learning-driven flood susceptibility mapping: comparing model performances and feature influences in a coastal watershed of the Eastern mediterranean

Flood susceptibility mapping is crucial for effective disaster management and mitigation in regions prone to extreme weather events. Despite advances in the use of conventional models for flood mapping, coastal basins in the Eastern Mediterranean still lack studies that employ machine learning (ML) techniques to accurately estimate flood risk. Most research focuses on quantitative prediction without systematically elucidating the influencing environmental factors, limiting the effectiveness of the results in applied contexts. Hence, the need to develop machine learning-based explanatory models that not only increase prediction accuracy but also reveal the most important determinants of flooding in complex coastal environments. This study assesses flood susceptibility using three ML algorithms, including, Light Gradient Boosting Machine (LGBM), Artificial neural networks (ANN), and eXtreme Gradient Boosting (XGBoost), with feature importance analysis conducted using SHapley Additive exPlanations (SHAP). The XGBoost model demonstrated superior performance in identifying flood susceptibility areas, compared to ANN, and LGBM. Feature importance analysis revealed that Aspect, Distance from River, and Slope were the most influential features, with Aspect being the most dominant predictor of flood susceptibility. The methodological integration of XGBoost with SHAP not only achieved high performance, but also provided interpretable insights into environmental drivers of flood risk. This combination represents a significant advancement over prior approaches, as it bridges predictive power with explainability, thereby enhancing the reliability of flood susceptibility mapping. The findings contribute to improved flood risk management strategies and lay the foundation for future research on interpretable and robust ML models for flood prediction.