한국농공학회논문집 (Journal of The Korean Society of Agricultural Engineers)

  • 제57권2호
  • /
  • Pages.37-45
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  • 2015
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  • 1738-3692(pISSN)
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  • 2093-7709(eISSN)
한국농공학회 (The Korean Society of Agricultural Engineers)
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한강수계에서의 부유사 예측을 위한 LOADEST 모형의 회귀식의 평가

Evaluation of Regression Models in LOADEST to Estimate Suspended Solid Load in Hangang Waterbody

  • Park, Youn Shik (Department of Rural Construction Engineering, Kongju National University) ;
  • Lee, Ji Min (Regional Infrastructure Engineering, Kangwon National University) ;
  • Jung, Younghun (Regional Infrastructure Engineering, Kangwon National University) ;
  • Shin, Min Hwan (Regional Infrastructure Engineering, Kangwon National University) ;
  • Park, Ji Hyung (National Institute of Environmental Research) ;
  • Hwang, Hasun (National Institute of Environmental Research) ;
  • Ryu, Jichul (National Institute of Environmental Research) ;
  • Park, Jangho (Regional Infrastructure Engineering, Kangwon National University) ;
  • Kim, Ki-Sung (Regional Infrastructure Engineering, Kangwon National University)
  • 투고 : 2014.10.28
  • 심사 : 2015.02.11
  • 발행 : 2015.03.31
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초록

Typically, water quality sampling takes place intermittently since sample collection and following analysis requires substantial cost and efforts. Therefore regression 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 LOADEST were evaluated in the study. Suspended solids data were collected from forty-nine stations from the Water Information System of the Ministry of Environment. Suspended solid data from each station was divided into two groups for calibration and validation. Nash-Stucliffe efficiency (NSE) and coefficient of determination ($R_2$) were used to evaluate estimated suspended solid loads. The regression models numbered 1 and 3 in LOADEST provided higher NSE and $R_2$, compared to the other regression models. The regression modes numbered 2, 5, 6, 8, and 9 in LOADEST provided low NSE. In addition, the regression model selected by LOADEST did not necessarily provide better suspended solid estimations than the other regression models did.

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