A fast and robust simulation-optimization methodology for stormwater quality management

Abstract

The simulation and optimization of low impact development (LID) practices has been a key research topic in stormwater management. In this study, a fast and robust framework was proposed for providing the optimal design of LID practices by coupling a physically based model, the Markov chain, with the multi-objective shuffled frog leaping algorithm (MOSFLA). The proposed framework was then tested for chemical oxygen demand (COD) reduction in a typical urban catchment in China. The storm water management model (SWMM) was used to provide the flow/COD data during the baseline scenario, and the Markov chain method was then incorporated as a subset of the physically based model. Based on the results, the computational efficiency was improved by 500 times when the new framework was used, and the robustness of the optimal results increased over 50% compared to commonly used algorithms. The relative error between the SWMM and the Markov chain method was less than 5%, indicating that a satisfactory performance could be obtained using the proposed framework. This new method provides a useful tool for optimizing LID practices and green infrastructure, especially for complex urban catchments.