Research on Multiobjective Optimization of Sponge City Based on SWMM Model

Abstract

With the acceleration of urbanization, the hardened area of the city is increasing, the rainwater runoff is increasing, and the pressure on the urban drainage network is increasing, resulting in urban waterlogging which seriously endangers people's travel safety. SC (sponge city) should take the city as the catchment area, and through the self-regulation of the water system, reduce the consumption of water resources and the discharge of pollutants into the water environment to the maximum extent, meet the production and life of residents to the maximum extent, adapt to the climate change, and serve the ecological balance. Based on SWMM (stormwater management model) model, this study constructed an SC planning and design method based on the coupling of multiobjective optimization and comprehensive evaluation and established a multi-model based on fast classification and non-dominated genetic algorithm (NSGA-II). The results show that compared with the results calculated by other methods, the multiobjective comprehensive optimization model established in this study and the results calculated by the genetic algorithm NSGA-II shorten the optimization period by 1.44%, improve the quality reliability by 9.86%, reduce the total cost by 0.66%, and reduce the environmental impact by 0.93%. The conclusion shows that it reduces the average discharge flow of the discharge port, relieves the drainage pressure of the pipe network, reduces the risk of waterlogging in the city, etc., and it is more suitable for dealing with rainfall events in medium and small return periods.

A Coupled Parameter Automation Calibration Module for Urban Stormwater Modelling

“…The GA's key steps include solution vector encoding, population of solution vectors, fitness function evaluation, genetic operator selection, crossover, and mutation [20], with each solution corresponding to an individual in a biological population. Owing to its global search capability, the GA has extensive applications [21] and is commonly used in practical problems such as automatic control, computer science, and fault diagnosis. It is suitable for solving complex nonlinear and multidimensional optimisation problems.…”