A new LID spatial allocation optimization system at neighborhood scale: Integrated SWMM with PICEA-g using MATLAB as the platform.

Despite the growing interest, limited studies have been conducted on LID spatial allocation optimization (SAO) at neighborhood scale, and no study has applied modifications to the optimization algorithm to improve its performance. In this study, such a new LID SAO system was proposed, which integrated a hydrological computing engine (SWMM) with an optimization algorithm (PICEA-g) using a programming language (MATLAB) as the platform. The specific modifications to the PICEA-g algorithm include: new methodologies for initializing candidate solutions, defining goal vector boundaries and enhanced genetic operators. The new LID SAO system was tested in a typical urban residential neighborhood in western Canada, and optimal solutions for LID implementation (bioretention, rain garden, permeable pavement and green roof) were obtained. The results showed that promising hydrologic benefits of reducing peak flow rate and total volume of stormwater runoff from the catchment, can be achieved with a relatively low cost. The LID SAO system provides users with flexibility and feasibility for a variety of drainage locations, scales and objectives (e.g., water quality).