A novel machine learning-based framework to extract the urban flood susceptible regions

•A ML based-framework is developed for spatiotemporally assessing UFS.•Temporal features of UFS are used to correct the spatial pattern.•SHAP are applied to quantify the contributions of driving variables.•The analysis reveals trend characteristics and future persistence of UFS.•Spatial identification of regions susceptible to UF is accomplished. The frequent occurrence of urban floods (UFs) poses significant threats to citizens’ lives and the national economy. Utilizing machine learning to assess urban flood susceptibility (UFS) provides valuable decision support for UF management. However, the precision of current studies is usually influenced by the variability of temporal factors like extreme rainfall, which limits the accurate identification of urban flood-susceptible regions (UFSRs). To address this issue, we present a novel approach that leverages the spatiotemporal distribution and characteristics of UFS to accurately identify UFSRs. In our case study of the Greater Bay Area (GBA) in China, we employed the Random Forest to assess the spatiotemporal distribution of UFS. We then used the Savitzky-Golay filter to correct UFS data based on the UFS time series from 2011 to 2020. The Theil-Sen median slope, Mann-Kendall test, and Hurst analysis were used to explore the spatiotemporal patterns of UFS. Shapley additive explanation was applied to quantify the contribution of selected variables. Our findings include: (1) UFS in the GBA demonstrates a rising trend, with high susceptibility areas increasing from 6.3 % in 2011 to 7.4 % in 2020; (2) UFSRs, covering approximately 11 % of the GBA, are primarily concentrated in the cities located around the central GBA; and (3) human behavior factors have a more significant influence on UF than natural ones. We believe the presented framework for the accurate extraction of UFSRs provides valuable decision support for sustainable city development.