Date of Award

3-6-2019

Publication Type

Master Thesis

Department

Civil and Environmental Engineering

Keywords

low impact development, Multiobjective optimization, risk, stormwater, urban flood

Supervisor

Tirupati Bolisetti

Rights

info:eu-repo/semantics/openAccess

Abstract

Increased urbanization and a changing climate are contributing to an increased urban flood risk. Low Impact Development (LID) is a green infrastructure approach to help mitigate this risk. Analysis of flooding potential and socioeconomic factors of an urban area are essential in determining how to best implement these controls. The objectives of the study was to identify the most prominent areas for LID implementation and develop a framework for identifying LID controls within a multiobjective optimization framework. Coupled risk assessment and socioeconomic analysis was used to determine the potential areas to implement LID controls to achieve continuous benefits. A risk assessment methodology was developed to delineate the greatest flooding risk areas in sewersheds. A socioeconomic analysis framework was then adapted to assess the areas that would be most likely to adopt and successfully maintain LID controls. A simulation-optimization framework was then developed by coupling Stormwater Management Model (SWMM 5) with the Borg Multiobjective Evolutionary Algorithm (MOEA). This methodology analyzed different LID implementation solutions with a cost function to determine the most cost effective LID solutions. The PCSWMM interface was used to create the model for a large urban sewershed in Windsor, Ontario, Canada. The model tested LID measures against eight different scenarios consisting of both historical climate data and future predicted climate change data with the objectives of reducing peak flows in the sewer system, reducing total runoff across the sewershed and minimizing the cost of LID implementation. The results provide stormwater management professionals and decision makers cost-benefit information for different LID implementation scenarios to help assess the feasibility of LID in this area and to make infrastructure investment decisions.

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