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 Sorting 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 objective 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 watershed in State College, Pennsylvania. Beyond multi-objective analysis, this paper also contributes improved SWMM representations of LID alternatives and demonstrates their usefulness for screening alternative site layouts for LID technologies.

Evolutionary and Holistic Assessment of Green-Grey Infrastructure for CSO Reduction

“…The study of [54] couples SWMM5 with NSGA-II to size and distribute drainage solutions, using decentralized and centralized measures for flood reduction, considering cost minimization and enhancement of ecosystem services. In another work, Zhang et al [55] applied multi-objective optimization to identify cost-effective practices in a watershed to be developed. Finally, Ahiablame et al [56] presented a review of effectiveness of sustainable measures and among the gaps identified they call for the necessity of watershed and regional scale evaluations, and the development of practical decision making tools.…”

Application of Stormwater Runoff Estimation Methods in the Case of Faculty of Agriculture Main Campus

“…The most advanced tool is EPA's Storm Water Management Model (SWMM) [27]. It is a dynamic rainfall-runoff and hydraulic simulation engine that was designed to predict the resultant runoff in urban areas from each modeled subcatchment in response to precipitation input [28,29,30,31]. Research conducted on estimating surface runoff volumes in major cities of Turkey has commonly concluded that tight resource management aiming to use alternative water resources such as rainwater are required for better environment, health and economy [32,33,34,35,36,37,38].…”

Application of BMP to urban runoff control using SUSTAIN model: Case study in an industrial area

“…PerezPedini et al (2005) described a distributed hydrologic model combined with a genetic algorithm to determine the optimal location of infiltration-based BMP. Zhang (2006) used a revised version of the non-dominated sorted genetic algorithm-II (NSGA-II) and epsilon non-dominated sorted genetic algorithm to optimize various low impact development designs in an urbanizing watershed. Oraei Zare et al (2012) studied technologies that coupled the storm water management model (SWMM) with NSGA-II to control both the quality and quantity of urban floods.…”