Achieving Optimal Allocation of Urban Flood Control Funds: Integrating Hydrodynamic Model-Based Risk Assessment with Diverse Multi-Objective Optimization Models

Anfeng Zhu^1,2Jiahao Zhong³Yinxiang Xu⁴Jingtao Hao³Yongkang Ma³Zegen Wang^5*

• ¹College of Artificial Intelligence, Zhejiang College of Security Technology, Wenzhou 325035, China, Wenzhou Zhejiang, China
• ²Wenzhou Future City Research Institute, Wenzhou 325000, China
• ³School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu 610500, China
• ⁴Sichuan University State Key Laboratory of Hydraulics and Mountain River Engineering, Chengdu, China
• ⁵School of Geoscience and Technology, Southwest Petroleum University, Chengdu, China

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 rely primarily on historical data at the city level. However, this method often leads to inefficient and misaligned fund distribution, as it fails to account for the complex, high-dimensional nature of flood risk scenarios and the lack of systematic comparative studies on algorithmic adaptability, how well different algorithms converge to optimal solutions, and handle the high-dimensional, discrete search space characteristic of fund allocation problems. To address this issue, this study develops a refined flood risk assessment system by integrating a coupled hydrological-hydrodynamic model with socioeconomic and infrastructure data. Several multi-objective optimization algorithms are then applied to identify the most cost-effective funding allocation strategy in high-dimensional scenarios. Results show that the model accurately identifies localized high-risk areas, such as river bends and the zones where steep slopes meet the plain, shifting the allocation strategy from “regional coverage” to “targeted risk-based precision.” Notable differences were observed among optimization algorithms: the SPEA2 algorithm achieved optimal 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, clarifies how algorithmic features influence funding efficiency, and provides actionable insights for enhancing urban flood resilience and sustainable development.