There is a need to consider people’s welfare when formulating policies, where subjective well-being is a proxy for welfare. Although it is important to elucidate the mechanisms underlying the decline in subjective well-being associated with flood experience when making policy, this has not been studied in detail. Therefore, this study sought to clarify the relationship between flood experience and subjective well-being, focusing on anxiety about floods. We conducted an online questionnaire in Tochigi Prefecture, Japan and analyzed the results of 2,630 respondents. Mediation analysis revealed that flood experience does not have a direct effect on subjective well-being (p < 0.05) but exerts a negative effect through anxiety about floods. The same was true when only people with recent flood experience were analyzed. This study suggests methods for restoring subjective well-being to its original level after a flood, such as managing anxiety about floods.

Predicting flash flood-prone areas is essential for proactive disaster management. ‎However, such predictions are challenging to obtain accurately with physical hydrological models owing to the scarcity of flood observation stations and the lack of monitoring systems. This study aims ‎to compare machine learning (ML) models (Random Forest, ‎Light, and CatBoost) and the Personal ‎Computer Storm Water Management Model ‎‎(PCSWMM) hydrological ‎model to predict flash flood susceptibility ‎‎maps (FFSMs) in an arid region (Wadi Qows in ‎Saudi Arabia). Nine independent factors ‎that influence FFSMs in the study area were ‎assessed. ‎Approximately 300 flash flood sites were identified through a post-flood survey after the ‎‎extreme flash floods of 2009 in Jeddah city. The dataset was randomly split into 70 percent for training and 30 percent for testing. The results ‎show that the area ‎under the receiver operating curve (ROC) values were ‎above 95% for all tested ‎models, indicating evident accuracy. The FFSMs developed by the ML ‎‎methods show acceptable agreement with the flood inundation map created using the ‎PCSWMM in terms of flood extension. Planners and officials can use the outcomes of this study to improve the mitigation measures for flood-prone regions in Saudi Arabia.