Journal of Environmental Impact Assessment (환경영향평가)

Korean Society of Environmental Impact Assessment (한국환경영향평가학회)
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Evaluation of Water Quality Prediction Models at Intake Station by Data Mining Techniques

데이터마이닝 기법을 적용한 취수원 수질예측모형 평가

  • Kim, Ju-Hwan (K-water Research Institute, Korea Water Resources Corp.) ;
  • Chae, Soo-Kwon (Department of Environmental Health and Safety, Eulji University) ;
  • Kim, Byung-Sik (Disaster Prevention in Urban Environments, Kangwon National University)
  • 김주환 (한국수자원공사 K-water연구원) ;
  • 채수권 (을지대학교 보건환경안전과) ;
  • 김병식 (강원대학교 도시환경방재전공)
  • Received : 2011.08.20
  • Accepted : 2011.10.02
  • Published : 2011.10.31
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Abstract

For the efficient discovery of knowledge and information from the observed systems, data mining techniques can be an useful tool for the prediction of water quality at intake station in rivers. Deterioration of water quality can be caused at intake station in dry season due to insufficient flow. This demands additional outflow from dam since some extent of deterioration can be attenuated by dam reservoir operation to control outflow considering predicted water quality. A seasonal occurrence of high ammonia nitrogen ($NH_3$-N) concentrations has hampered chemical treatment processes of a water plant in Geum river. Monthly flow allocation from upstream dam is important for downstream $NH_3$-N control. In this study, prediction models of water quality based on multiple regression (MR), artificial neural network and data mining methods were developed to understand water quality variation and to support dam operations through providing predicted $NH_3$-N concentrations at intake station. The models were calibrated with eight years of monthly data and verified with another two years of independent data. In those models, the $NH_3$-N concentration for next time step is dependent on dam outflow, river water quality such as alkalinity, temperature, and $NH_3$-N of previous time step. The model performances are compared and evaluated by error analysis and statistical characteristics like correlation and determination coefficients between the observed and the predicted water quality. It is expected that these data mining techniques can present more efficient data-driven tools in modelling stage and it is found that those models can be applied well to predict water quality in stream river systems.

Keywords

References

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