Advancing Urban Flood Risk Mapping: A Hybrid Framework Integrating Interpretable Machine Learning and Uncertainty-Aware Expert Judgment

Climate change and urbanization are intensifying urban flood risks worldwide, making flood risk management critically important. While machine learning has been widely applied for flood susceptibility mapping, many studies do not integrate socioeconomic dimensions for comprehensive risk assessment; moreover, traditional subjective evaluation methods provide insufficient quantification of expert judgment uncertainty. This study presents a hybrid framework for urban flood risk mapping, which integrates interpretable machine learning and the Z-number-based Fuzzy Analytic Hierarchy Process (Z-FAHP), applied to Tokyo, Japan, utilizing a high-resolution digital elevation model (DEM) derived from airborne LiDAR and other publicly available geospatial datasets. The framework leverages machine learning efficiency while better accommodating vague linguistic expert judgments by incorporating confidence levels. Flood susceptibility was derived through machine learning with 14 features, with model performance rigorously evaluated using both non-spatial and spatial 4-fold cross-validation, and the CatBoost model demonstrated optimal performance. SHAP (SHapley Additive exPlanations) analysis was further employed to enhance model transparency by quantifying feature contributions. Exposure and vulnerability were quantified from 3 and 5 socioeconomic indicators, respectively, through Z-FAHP. These three criteria were synthesized to produce the flood risk map using simple additive weighting. Results reveal that approximately 32.9% of the study area faces high-to-very-high flood risk, concentrated in eastern lowlands and river corridors. This transferable framework can be applied to other cities globally as an effective tool for urban flood risk management.