Youngkyu Lee

This study aims to enhance the accuracy and interpretability of flood susceptibility mapping (FSM) in Seoul, South Korea, by integrating automated machine learning (AutoML) with explainable artificial intelligence (XAI) techniques. Ten topographic and environmental conditioning factors were selected as model inputs. We first employed the Tree-based Pipeline Optimization Tool (TPOT), an evolutionary AutoML algorithm, to construct baseline ensemble models using Gradient Boosting (GB), Random Forest (RF), and XGBoost (XGB). These models were further fine-tuned using Bayesian optimization via Optuna. To interpret the model outcomes, SHAP (SHapley Additive exPlanations) was applied to analyze both the global and local contributions of each factor. The SHAP analysis revealed that lower elevation, slope, and stream distance, as well as higher stream density and built-up areas, were the most influential factors contributing to flood susceptibility. Moreover, interactions between these factors, such as built-up areas located on gentle slopes near streams, further intensified flood risk. The susceptibility maps were reclassified into five categories (very low to very high), and the GB model identified that approximately 15.047% of the study area falls under very-high-flood-risk zones. Among the models, the GB classifier achieved the highest performance, followed by XGB and RF. The proposed framework, which integrates TPOT, Optuna, and SHAP within an XAI pipeline, not only improves predictive capability but also offers transparent insights into feature behavior and model logic. These findings support more robust and interpretable flood risk assessments for effective disaster management in urban areas.

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.