Author(s): Wei Lu, Xiaosheng Qin, Jianjun Yu
Linked Author(s): Xiaosheng Qin
Keywords: Storm water detention tank, flood mitigation, optimization, genetic algorithm (GA), artificial neural network
Abstract: Stormwater detention tank is widely used as an effective measure in improving the performance of an urban stormwater drainage system (USDS) and mitigating urban flood risk during intensive storm events. Different layouts of detention tanks can largely affect their efficiency; therefore, it is crucial to determine their appropriate locations. This study investigates the feasibility of integrating urban hydrological simulation into an optimization model, meanwhile taking into consideration the constraints of realistic flooding control criteria, for the purpose of developing a general methodology for the optimal location design of detention tanks. For demonstration of the proposed method, the Storm Water Management Model (SWMM) is run on a hypothetical urban catchment, concerning different specifications of detention tanks in the USDS. The performance criteria of the nominated detention tanks are set as the computed value of the total flood volume in the USDS, as these criteria are closely connected to the loss ratio of property assets and infrastructures in an urban flood event. An artificial neural network (ANN) model is then established as an emulator to save the computational effort in hydrological simulation. Genetic algorithm (GA) is employed to automatically search the optimal solution in the optimization process. The optimal design of detention tanks could lead to a reasonable reduction of flood volume with a relatively lower cost when compared to an empirical design. The proposed study could help water management authorities or governmental agencies in reaching a more cost-effective decision-making
Year: 2017