유전자 알고리즘과 회귀식을 이용한 오염부하량의 예측
Estimation of Pollutant Load Using Genetic-algorithm and Regression Model
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BACKGROUND: Water quality data are collected lessfrequently than flow data because of the cost to collect andanalyze, while water quality data corresponding to flowdata are required to compute pollutant loads or to calibrateother hydrology models. Regression models are applicableto interpolate water quality data corresponding to flow data. METHODS AND RESULTS: A regression model wassuggested which is capable to consider flow and timevariance, and the regression model coefficients werecalibrated using various measured water quality data withgenetic-algorithm. Both LOADEST and the regressionusing genetic-algorithm were evaluated by 19 water qualitydata sets through calibration and validation. The regressionmodel using genetic-algorithm displayed the similar modelbehaviors to LOADEST. The load estimates by bothLOADEST and the regression model using genetic-algorithmindicated that use of a large proportion of water quality datadoes not necessarily lead to the load estimates with smallererror to measured load. CONCLUSION: Regression models need to be calibratedand validated before they are used to interpolate pollutantloads, as separating water quality data into two data sets forcalibration and validation.
Abstract
BACKGROUND: Water quality data are collected lessfrequently than flow data because of the cost to collect andanalyze, while water quality data corresponding to flowdata are required to compute pollutant loads or to calibrateother hydrology models. Regression models are applicableto interpolate water quality data corresponding to flow data. METHODS AND RESULTS: A regression model wassuggested which is capable to consider flow and timevariance, and the regression model coefficients werecalibrated using various measured water quality data withgenetic-algorithm. Both LOADEST and the regressionusing genetic-algorithm were evaluated by 19 water qualitydata sets through calibration and validation. The regressionmodel using genetic-algorithm displayed the similar modelbehaviors to LOADEST. The load estimates by bothLOADEST and the regression model using genetic-algorithmindicated that use of a large proportion of water quality datadoes not necessarily lead to the load estimates with smallererror to measured load. CONCLUSION: Regression models need to be calibratedand validated before they are used to interpolate pollutantloads, as separating water quality data into two data sets forcalibration and validation.
참고문헌 (15)
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