한강수계에서의 부유사 예측을 위한 LOADEST 모형의 회귀식의 평가
Evaluation of Regression Models in LOADEST to Estimate Suspended Solid Load in Hangang Waterbody
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참고문헌
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초록
Typically, water quality sampling takes place intermittently since sample collection and following analysis requires substantial cost and efforts. Thereforeregression models (or rating curves) are often used to interpolate water quality data. LOADEST has nine regression models to estimate water quality data,and one regression model needs to be selected automatically or manually. The nine regression models in LOADEST and auto-selection by LOADESTwere evaluated in the study. Suspended solids data were collected from forty-nine stations from the Water Information System of the Ministry ofEnvironment. Suspended solid data from each station was divided into two groups for calibration and validation. Nash-Stucliffe efficiency (NSE) andcoefficient of determination (R2) were used to evaluate estimated suspended solid loads. The regression models numbered 1 and 3 in LOADEST providedhigher NSE and R2, compared to the other regression models. The regression modes numbered 2, 5, 6, 8, and 9 in LOADEST provided low NSE. Inaddition, the regression model selected by LOADEST did not necessarily provide better suspended solid estimations than the other regression models did.
Abstract
Typically, water quality sampling takes place intermittently since sample collection and following analysis requires substantial cost and efforts. Thereforeregression models (or rating curves) are often used to interpolate water quality data. LOADEST has nine regression models to estimate water quality data,and one regression model needs to be selected automatically or manually. The nine regression models in LOADEST and auto-selection by LOADESTwere evaluated in the study. Suspended solids data were collected from forty-nine stations from the Water Information System of the Ministry ofEnvironment. Suspended solid data from each station was divided into two groups for calibration and validation. Nash-Stucliffe efficiency (NSE) andcoefficient of determination (R2) were used to evaluate estimated suspended solid loads. The regression models numbered 1 and 3 in LOADEST providedhigher NSE and R2, compared to the other regression models. The regression modes numbered 2, 5, 6, 8, and 9 in LOADEST provided low NSE. Inaddition, the regression model selected by LOADEST did not necessarily provide better suspended solid estimations than the other regression models did.
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