Author(s): Ruixuan Wu; Reza Ahamadian; Changbo Jiang
Linked Author(s): Changbo Jiang
Keywords: Urban flood resilience Integrated green-frey-blue infrastructures Layout design SWMM Genetic algorithm critical are
Abstract: Influenced by climate change and urbanization on a global scale, urban flooding will become more severe in the coming decades, with a further increase in frequency and scale of occurrence, which will expose more urban populations to extreme risks. The integrated green-gray-blue infrastructure (GGBI) proposed in recent years is considered a more sustainable alternative that fully takes advantage of the synergistic effect between different types of infrastructure. Many studies have shown that it effectively controls runoff and pollutants and improves cities’ resilience to flooding. The placement of GGBIs is key to achieving flood protection goals. The complexities of what types of facilities to choose, where to place them, and how to combine them can often lead to design falling into the trap of costing a great deal of money with little benefit. Hydrodynamic models are a reliable tool for simulating urban flooding, but researchers tend to design schemes based on personal experience, which is not cost-effective. Fortunately, optimization algorithms such as genetic algorithms have been proposed to provide newer and more effective ideas for the placement of GGB and make hydrodynamic models more useful. On the other hand, current research tends to take a “decision-maker” thinking that focuses on the entire urban area, which sacrifices the possibility of flexible decision-making by different stakeholders, as the areas or buildings of particular interest to them may be different. This study proposes a framework for designing flood protection schemes for key areas based on genetic algorithm optimization. We start from the core interests of a certain stakeholder to identify the key area or building and design the arrangement of GGB for this area. Runoff control and total investment cost are selected as objective functions, then schemes will be simulated by SWMM and optimized by genetic algorithm. After obtaining the Pareto front, we choose schemes that can effectively prevent flooding and enter the simulation results as input conditions into a 2D simulation model to simulate surface runoff in this area. Water depth and velocity will be used as evaluation indicators to evaluate these schemes, the best scheme can be selected by comprehensive evaluation. This paper aims to propose a design framework for arranging flood protection measures for key areas, which allows different stakeholders to protect their core interests. Moreover, this study will summarize the general rules of arranging flood protection measures to guide the practice of flood protection and urban construction.
Year: 2025