Achieving optimal allocation of urban flood control funds: integrating hydrodynamic model-based risk assessment with diverse multi-objective optimization models

Against the backdrop of global climate change, the increasing frequency of flooding events is imposing greater demands on urban flood prevention systems. Traditional approaches to allocating flood control funds, which rely primarily on historical city-level data, often lead to inefficient and misaligned distribution. This inefficiency stems from an inability to account for complex, high-dimensional flood risk scenarios, as well as a lack of systematic comparison of algorithmic performance in terms of adaptability, convergence to optimal solutions, and handling of high-dimensional discrete search spaces. To address this issue, this study first developed a refined flood risk assessment system by integrating a coupled hydrological-hydrodynamic model with socioeconomic and infrastructure data. Subsequently, several multi-objective optimization algorithms were applied to this system to identify the most cost-effective funding allocation strategy under high-dimensional scenarios. The model accurately identified localized high-risk areas, such as river bends and zones where steep slopes meet plains, thereby shifting the optimal allocation strategy from “regional coverage” to “targeted risk-based precision.” Notable differences were observed among the optimization algorithms. Specifically, the SPEA2 algorithm achieved optimal overall benefits while reducing the proportion of extremely high-risk areas to 0.02%. This study highlights the mechanistic advantages of hydrological-hydrodynamic models in pinpointing flood risks and clarifies how algorithmic features influence funding allocation efficiency. The findings provide actionable insights for enhancing urban flood resilience and supporting sustainable development.