Multi-Objective Optimization of Low Impact Development Designs in an Urbanizing Watershed

Abstract

Multi-objective optimization linked with an urban stormwater model is used in this study to identify cost-effective low impact development (LID) implementation designs for small urbanizing watersheds. The epsilon-Non-Dominated Sort- ing Genetic Algorithm II (ε-NSGAII) has been coupled with the US Environmental Protection Agency’s Stormwater Management Model (SWMM) to balance the costs and the hydrologic benefits of candidate LID solutions. Our objec- tive in this study is to identify the near-optimal tradeoff between the total LID costs and the total watershed runoff volume constrained by pre-development peak flow rates. This study contributes a detailed analysis of the costs and benefits associated with the use of green roofs, porous pavement, and bioretention basins within a small urbanizing wa- tershed in State College, Pennsylvania. Beyond multi-objective analysis, this paper also contributes improved SWMM repre- sentations of LID alternatives and demonstrates their usefulness for screening alternative site layouts for LID technologies.