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Estimation of Pollutant Load Using Genetic-algorithm and Regression Model
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 Title & Authors
Estimation of Pollutant Load Using Genetic-algorithm and Regression Model
Park, Youn Shik;
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BACKGROUND: Water quality data are collected less frequently than flow data because of the cost to collect and analyze, while water quality data corresponding to flow data are required to compute pollutant loads or to calibrate other hydrology models. Regression models are applicable to interpolate water quality data corresponding to flow data. METHODS AND RESULTS: A regression model was suggested which is capable to consider flow and time variance, and the regression model coefficients were calibrated using various measured water quality data with genetic-algorithm. Both LOADEST and the regression using genetic-algorithm were evaluated by 19 water quality data sets through calibration and validation. The regression model using genetic-algorithm displayed the similar model behaviors to LOADEST. The load estimates by both LOADEST and the regression model using genetic-algorithm indicated that use of a large proportion of water quality data does not necessarily lead to the load estimates with smaller error to measured load. CONCLUSION: Regression models need to be calibrated and validated before they are used to interpolate pollutant loads, as separating water quality data into two data sets for calibration and validation.
Genetic-Algorithm;LOADEST;Pollutant Load Estimation;Regression Model;
 Cited by
한강수계에서의 부유사 예측을 위한 LOADEST 모형의 회귀식의 평가,박윤식;이지민;정영훈;신민환;박지형;황하선;류지철;박장호;김기성;

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