Exploring the optimal method for quantifying the contribution of driving factors of urban floods

• We explore the most suitable method for quantifying the contribution of factors to UF. • The relationship of driving factors to UF show non-linearity and obvious spatial heterogeneity. • SHAP based on machine learning rules is the most suitable way for the quantification. Amid climate change and urban expansion, urban floods (UFs) have become a significant concern. Effective UF management requires two key elements: (1) assessing susceptibility to identify flood-prone hotspots and (2) analyzing factor contributions to pinpoint primary drivers. While research on urban flood susceptibility (UFS) is well-established, studies on factor contributions usually limited to the simple application without fully evaluating their alignment with UF characteristics. This study addresses this gap by examining Shenzhen, a rapidly urbanizing city, and applying various regression methods (linear, multiple machine learning (ML), and Shapley Additive exPlanations (SHAP)) to evaluate flood-driving factors. Through accuracy assessments and environmental considerations, we identify the most suitable methods for this analysis. Our findings include: (1) Random Forest (RF)-based UFS assessment reveals significant spatial heterogeneity, with higher UFS in central-western areas and lower risks in the southeast; (2) the most contributing factors in Shenzhen exhibit non-linear and non-parametric effects on UF, supporting the use of RF; (3) we found substantial spatial heterogeneity in factor contributions, necessitating SHAP analysis to capture local variations and provide tailored management insights. Thus, this study integrates methodological rigor with urban environmental context, enhancing factor contribution analysis for UF and supporting effective management strategies.