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Watershed optimization of best management practices using AnnAGNPS and a genetic algorithm
Author(s) -
Srivastava P.,
Hamlett J. M.,
Robillard P. D.,
Day R. L.
Publication year - 2002
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2001wr000365
Subject(s) - nonpoint source pollution , watershed , pollution , environmental science , water quality , agriculture , genetic algorithm , pollutant , water pollution , environmental engineering , algorithm , computer science , ecology , biology , machine learning
An optimization algorithm linked with a nonpoint source (NPS) pollution model can be used to optimize NPS pollution control strategies on a field‐by‐field basis in a watershed by maximizing NPS pollution reduction and net monetary return. In this paper a methodology is described which integrated a genetic algorithm (GA) (an optimization algorithm) with a continuous simulation, watershed‐scale, NPS pollution model, Annualized Agricultural Non‐Point Source Pollution model (AnnAGNPS) to optimize the selection of best management practices (BMP) on a field‐by‐field basis for an entire watershed. To test the methodology, optimization analysis was performed for a U.S. Department of Agriculture experimental watershed in Pennsylvania to identify BMPs that minimized long‐term (over a 4‐year period) water quality degradation and maximized net farm return on an annual basis. Results indicate that the GA was able to identify BMP schemes that reduced pollutant load by as much as 56% and increased net annual return by 109%.