Explainable AI-based risk assessment for pluvial floods over South Korea

Analytic Hierarchy Process (AHP) of pluvial flood risk assessment has been widely used, incorporating multiple assessment indices. However, uncertainty assessment of expert judgement-based flood risk remains limited. This study proposes a Machine Learning (ML) model-based AHP approach, using pluvial flood-related data of hazard, exposure, vulnerability, and capacity of South Korea over 2002-2021. In this study, the trained eXtreme Gradient Boosting (XGBoost) and Random Forest (RF) models successfully predict flood economic losses using 21 flood-related variables, outperforming LightGBM and CatBoost. Permutation importance scores from the trained XGBoost and RF models are used to estimate the mean and 95 % confidence intervals of the assessment factor weights. Both models show that rainfall amount, river area, population density, and green belt area are important factors for flood damage prediction, but the XGBoost (RF) model identifies impermeable areal fraction (river area) as the most important component in exposure, resulting in disparity in the uncertainty range in major cities over South Korea where the XGBoost and RF models show a high risk consistently. This study substantiates the practical application of the proposed ML based-AHP approach for uncertainty assessment of flood risk, highlighting the need for balanced land development and green infrastructure for flood mitigation.