Hydraulic optimization simulation for reducing confluence and controlling the overflow pollution of storage ponds based on the Storm Water Management Model and Non-dominated Sorting Genetic Algorithm-II

Abstract

The Storm Water Management Model (SWMM) was established to simulate rainfall–runoff dynamically, and the internal runoff component of the SWMM was used to simulate rainfall operation in each watershed, including rainfall–runoff and scour pollution load. Then, using the routing component in the SWMM, the properties of runoff into the tank system are calculated through pipelines and other facilities to obtain the optimal tank volume. The coupling optimization model was established, and the algebraic function of the storage capacity, total runoff, and total cost was established by using the multiple linear regression method, which was transformed into the optimization model aiming at the minimum total runoff and total cost. The NSGA-II is improved by using a reverse learning mechanism. By solving the optimization model, the non-dominant solution of the proxy model is obtained. The non-dominant solution was substituted into the SWMM, and the rationality of the optimization results was analyzed. The experimental results show that the reservoir volume determined by this method can effectively accept the pollutants brought by the initial rain, so as to reduce the hydraulic pollution caused by the confluence overflow and the overflow pollution of the urban integrated pipe network.