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Water 2017, 9(9), 704;

Optimization of a Water Quality Monitoring Network Using a Spatially Referenced Water Quality Model and a Genetic Algorithm

Biological and Agricultural Engineering Department, Texas A & M University, 2117 TAMU, College Station, TX 77843, USA
Author to whom correspondence should be addressed.
Received: 14 June 2017 / Revised: 25 August 2017 / Accepted: 12 September 2017 / Published: 15 September 2017
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The monitoring network for a river system is designed to provide information about water quantity and quality. The development of Watershed Protection Plans and Total Maximum Daily Loads require systematic monitoring of waterbodies. In this study, optimum water quality monitoring networks were selected to assess E. coli loads in the Guadalupe River and San Antonio River basins. A Genetic Algorithm (GA) was applied to select monitoring stations using the mean annual E. coli flux from the Spatially Referenced Regression Model on Watershed Attributes (SPARROW). The objectives of the GA were to minimize the number of monitoring stations, include large values of the mean annual E. coli flux, and minimize uncertainty of the flux estimations. Constraints related to the monitoring of critical locations were included in a multi-objective optimization problem. The SPARROW model was applied to the optimized GA solution sets, which were compared using the objective values and statistical indices. The best GA-generated alternative set adequately represented the San Antonio River basin, in good agreement with a previous study conducted using only SPARROW. The application of the GA ensured the inclusion of the monitoring stations with large values of E. coli flux, which reflected high-risk areas within the watershed. View Full-Text
Keywords: bacteria; SPARROW model; TMDL; water quality; watershed management bacteria; SPARROW model; TMDL; water quality; watershed management

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Puri, D.; Borel, K.; Vance, C.; Karthikeyan, R. Optimization of a Water Quality Monitoring Network Using a Spatially Referenced Water Quality Model and a Genetic Algorithm. Water 2017, 9, 704.

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