신경망과 퍼지 알고리즘을 이용한 하천 수질예측

Water Quality Forecasting of River using Neural Network and Fuzzy Algorithm

  • 이경훈 (전남대학교 토목공학과) ;
  • 강일환 (경호엔지니어링(주) 상하수도부) ;
  • 문병석 (서남대학교 토목공학과) ;
  • 박진금 (대한주택공사 광주전남본부)
  • 투고 : 2004.12.17
  • 심사 : 2005.03.10
  • 발행 : 2005.04.30

초록

This study applied the Neural Network and Fuzzy theory to show water-purity control and preventive measure in water quality forecasting of the future river. This study picked out NAJU and HAMPYUNG as the subject of investigation and used monthly the water quality and the outflow data of KWANGJU2, NAJU, YOUNGSANNPO and HAMPYUNG from 1995 to 1999 to forecast BOD, COD, T-N, T-P water density. The datum from 1995 to 1999 are used for study and that of 2000 are used for verification. To develop model of water quality forecasting, firstly, this research formed Neural Network model and divided Neural Network model into two case - the case of considering lag and not considering. And this study selected optimal Neural Network model through changing the number of hidden layer based on input layer(n) from n to 3n. Through forecasting result, the case without considering lag showed more precise simulated result. Accordingly, this study intended to compare, analyse that Fuzzy model using the method without considering lag with Neural Network model. As a result, this study found that the model without considering lag in Neural Network Network shows the most excellent outcome. Thus this study examined a forecasting accuracy, analyzed result and verified propriety through appling the method of water quality forecasting using Neural Network and Fuzzy Algorithms to the actual case.

키워드

참고문헌

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