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Development of a multi-objective optimization tool for the selection and placement of BMPs for nonpoint source pollution control

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  2007 ASAE Annual Meeting  072105.(doi:10.13031/2013.23226)
Authors:   Chetan Maringanti, Indrajeet Chaubey, Jennie Popp
Keywords:   Multi-objective genetic algorithms, BMP, nonpoint source pollution, optimization

Nonpoint source (NPS) pollution from agricultural areas can be minimized by the implementation of best management practices (BMPs) at the source (farm), by controlling the movement of pollutants from the agricultural areas into the receiving bodies. However, selection and implementation of BMPs in every farm, to achieve cost effective NPS pollution reduction in a watershed may be a daunting task. This typically requires obtaining an optimal solution, from the many million solutions that are possible, that is ecologically effective and economically feasible for the placement of BMPs. The previous works done to solve this problem have used genetic algorithms (GA) for optimizing the two objectives of : 1) pollution reduction and 2) cost increase. But most of the works have considered the two objectives individually during the optimization process by introducing a constraint on the other objective. This approach of finding an optimal solution is not practical as the constrained objective results in a decrease in the degree of freedom in the solution space. In the present work the optimization is performed by considering the two objectives simultaneously. A multi-objective genetic algorithm (NSGA-II) was used to optimize the two objectives which gave a tradeoff between the two objectives for a range of optimal pollution reduction alternatives and their corresponding cost for implementation of BMPs. The model was used for the selection and placement of BMPs in L’Anguille River Watershed, Arkansas, USA for total phosphorus (TP) reduction. The most ecologically effective solution from the model had a TP reduction of 33% from the base scenario for a BMP implementation cost of $14 million. The tradeoff was obtained between the two optimized objective functions which can be used to achieve desired water quality goals with the minimum BMP implementation cost for the watershed.

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