An Online Data-Driven Evolutionary Algorithm–Based Optimal Design of Urban Stormwater-Drainage Systems

Abstract

To reduce flood risk in urban areas, an optimal design of drainage networks for urban areas is essential for flooding control and drainage management of the urban stormwater drainage system (USDS). A conventional design is generally used for drainage networks, resulting in high computational costs and limited flooding reduction effect. In this study, based on an on-line data-driven evolutionary algorithm coupled with the storm water management model (SWMM), a novel approach for USDS drainage network optimization design was developed. A case in Xi’an City, China, was then selected for practical implementation, where the performances of the local planning scheme, the particle swarm optimization algorithm (PSO) and the proposed approach were compared. Results confirmed that our proposed methodological approach is feasibility and highly efficiency, leading to a 32% reduction in total flooding from that resulting from the local planning scheme. In addition, the average computational time was reduced by 57%, while the flooding control effect was better, compared to PSO algorithm optimization. These results suggest that our optimization design approach is reliable and applicable, and can benefit and assist designers in practice.