Explainable Artificial Intelligence (Xai) for Flood Susceptibility Assessment in Seoul: Leveraging Evolutionary and Bayesian AutoML Optimization

This study aims to improve the accuracy and interpretability of flood susceptibility mapping (FSM) by integrating automated machine learning (AutoML) with explainable artificial intelligence (XAI) frameworks. Using spatial data from Seoul, South Korea, ten topographic and environmental factors were selected as model inputs. Initially, the Tree-based Pipeline Optimization Tool (TPOT), an evolutionary AutoML algorithm, was employed to construct baseline models using Gradient Boosting (GB), Random Forest (RF), and XGBoost (XGB). Subsequently, Bayesian optimization with Optuna was applied for hyperparameter tuning, resulting in enhanced performance, with the GB model achieving the highest AUC of 0.966. To ensure interpretability, SHAP (SHapley Additive exPlanations) was used to analyze both global and local feature contributions. The SHAP summary and dependence plots identified elevation (DEM), slope, stream distance, stream density, and built-up areas (LULC_7.0) as primary drivers of flood risk. Force and waterfall plots provided instance-level insights into model behavior, validating prediction logic across various geospatial contexts. In addition, Optuna’s visualization tools, such as optimization history, parallel coordinate plots, and hyperparameter importance graphs, offered transparency into how parameter spaces affect performance. These findings highlight the critical role of hyperparameter tuning in FSM and demonstrate the synergy of combining TPOT, Optuna, and SHAP within an XAI framework. The proposed approach not only improves predictive performance but also ensures interpretability, supporting more transparent and data-driven flood risk management.