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Current site scale stormwater management designs typically include multiple distributed stormwater best management practices (BMPs), necessary to meet regulatory objectives for nutrient removal and groundwater recharge. Selection of the appropriate BMPs for a particular site requires consideration of contributing drainage area characteristics, such as soil type, area, and land cover. Other physical constraints such as karst topography, areas of highly concentrated pollutant runoff, etc. as well as economics, such as installation and operation and maintenance cost must be considered. Due to these multiple competing selection criteria and regulatory requirements, selection of optimal configurations of BMPs by manual iteration using conventional design tools is not tenable, and the resulting sub-optimal solutions are often biased. This dissertation addresses the need for an objective BMP selection optimization tool through definition of an objective function, selection of an optimization algorithm based on defined selection criteria, development of cost functions related to installation cost and operation and maintenance cost, and ultimately creation and evaluation of a new software tool that enables multi-objective user weighted selection of optimal BMP configurations.
A software tool is developed using the nutrient and pollutant removal logic found in the Virginia Runoff Reduction Method (VRRM) spreadsheets. The resulting tool is tested by a group of stormwater professionals from the Commonwealth of Virginia for two case studies. Responses from case study participants indicate that use of the tool has a significant impact on the current engineering design process for selection of stormwater BMPs. They further indicate that resulting selection of stormwater BMPs through use of the optimization tool is more objective than conventional methods of design, and allows designers to spend more time evaluating solutions, rather than attempting to meet regulatory objectives. |
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