Use of Multiobjective Evolutionary Algorithm Optimization for Low-Impact Development Placement

Abstract

The transformation of natural land cover to urban areas severely alters the hydrologic flow regime of watersheds. The negative impacts include the increase of surface runoff and decrease of infiltration rates, which can result in more frequent and intense flood events and the reduction of groundwater recharge. Low Impact Developments (LIDs) are strategies designed to better mimic the natural flow regime by promoting higher infiltration and the treatment of stormwater. Examples of LID structures are bio-gardens, green roofs, bio-swales and pervious pavements. While the expansion of LIDs in urban catchments would be desirable, retrofitting large urban watersheds with LIDs can be cost prohibitive. This study combines hydrologic simulation with a Multi-Objective Evolutionary Algorithm (MOEA) to find solutions in terms of LID design and location in urban catchments that maximize the environmental benefits and characterize the tradeoffs between LID performance indicators and costs. The solutions are evaluated in terms of costs, peak flow, and the Hydrologic Footprint Residence (HFR). The HFR is a new metric designed to quantify the impacts of urbanization in the hydrologic regime by representing the dynamics of inundated areas and residence time of flood waves. The results provide a basis for better stormwater management and planning because tradeoff curves provide a wider spectrum of designs and placement guidelines, improving the sustainability of urban watersheds.

From Rain Tanks to Catchments: Use of Low-Impact Development To Address Hydrologic Symptoms of the Urban Stream Syndrome

“…A number of modeling tools are available for optimizing the selection and siting of LID technologies so as to minimize flood risk, maximize human and ecosystem cobenefits, and stay within capital, maintenance, and operation costs. − These optimization schemes have several elements in common, including: (1) a spatially explicit (e.g., GIS-based) platform that includes information on the informal and formal drainage for a site and candidate locations for LID technologies; (2) a rainfall-runoff model that routes stormwater through the catchment; (3) an objective function that quantifies hydrologic performance (e.g., relative to stormwater harvest and infiltration targets, see Section ) and costs of candidate LID configurations; and (4) an algorithm that identifies optimal solutions (e.g., by minimizing one or more objective functions) ,,− or finds the greatest unit improvement in stormwater control per unit incremental cost. − Examples include software packages developed by university researchers, ,, the Model for Urban Storm water Improvement Conceptualization (MUSIC), and the U.S. Environmental Protection Agency’s System for Urban Storm water Treatment and Integration (SUSTAIN)…”

Developing a stochastic conflict resolution model for urban runoff quality management: Application of info-gap and bargaining theories

“…Jia et al (2012), Lee et al (2012) and Yazdi and Neyshabouri (2014) are examples of who have worked on optimal design of LIDs in urban and suburban areas. To identify ideal LID design and location in urban catchments, characterizing the tradeoff curves between LID performance indicators and costs is crucial to defensible urban stormwater management decision-making (Giacomoni, 2015). Therefore, multi-objective optimization or multi-attribute decision making techniques have been employed in several studies to determine optimal LID implementation strategies for urban stormwater runoff quality and quantity control.…”