Journal of Korean Society of Water and Wastewater (상하수도학회지)

The Korean Society of Water and Wastewater (대한상하수도학회)
권호 표지

Real Time Water Quality Forecasting at Dalchun Using Nonlinear Stochastic Model

추계학적 비선형 모형을 이용한 달천의 실시간 수질예측

  • 연인성 (충북대학교 토목공학과) ;
  • 조용진 (충주대학교 환경공학과) ;
  • 김건흥 (인하대학교 환경토목공학부)
  • Received : 2005.08.11
  • Accepted : 2005.12.09
  • Published : 2005.12.15
Download PDF
⟨ Previous Next ⟩

Abstract

Considering pollution source is transferred by discharge, it is very important to analyze the correlation between discharge and water quality. And temperature also influent to the water quality. In this paper, it is used water quality data that was measured DO (Dissolved Oxygen), TOC (Total Organic Carbon), TN (Total Nitrogen), TP (Total Phosphorus) at Dalchun real time monitoring stations in Namhan river. These characteristics were analyzed with the water quality of rainy and nonrainy periods. Input data of the water quality forecasting models that they were constructed by neural network and neuro-fuzzy was chosen as the reasonable data, and water quality forecasting models were applied. LMNN (Levenberg-Marquardt Neural Network), MDNN (MoDular Neural Network), and ANFIS (Adaptive Neuro-Fuzzy Inference System) models have achieved the highest overall accuracy of TOC data. LMNN and MDNN model which are applied for DO, TN, TP forecasting shows better results than ANFIS. MDNN model shows the lowest estimation error when using daily time, which is qualitative data trained with quantitative data. If some data has periodical properties, it seems effective using qualitative data to forecast.

Keywords

References

  1. 김만식, 한재석 (2003) 인공지능기법을 이용한 수질모형의 최적 매개변수 추정연구, 환경관리학회지, 9(1), pp.1-9
  2. 김주식, 서동일, 이은형, 이준기 (2002) 금강수계 수질자동 측정망 추가설치를 위한 우선순위 분석, 대한상하수도학회 공동춘계 학술발표회 논문집, pp. 61-63
  3. 김주환 (2002) 갈수기 댐 하류 수질예측을 위한 신경망 모형의 적용, 한국수처리기술연구회지,10(4), pp.51-60
  4. 류병로, 한양수 (1998) ARIMA 모형에 의한 하천수질 예측, 한국환경과학학회지, 7(4), pp.433-440
  5. 안상진, 연규방, 연인성 (2000) 신경망 모형을 이용한 수칠예측과 시스템 구축, 대한상하수도학회 공동춘계 학술 발표회 논문집, pp,249-252
  6. 이경훈, 김진모, 문병석 (2001) BP 알고리즘을 이용한 호소수 수질예측, 대한상하수도학회 공동추계 학술발표회 논문집, pp.203-206
  7. 정세웅, 강병수, 조주영 (2003) 일별 암모니아성질소농도 예측을 위한 다중회귀모형 개발, 대한환경공학회 추계 학술연구발표회 논문집, pp.136-144
  8. 조용진, 연인성, 이재관(2004) 실시간 수질예측을 위한 신경망 모형의 적용, 한국물환경학회지. 20(4). pp.321-326
  9. 환경관리공단 (2001) 팔당호등 한강수계 수질자동측정망 확대설치타당성조사 결과보고서. pp, 44-46
  10. lang, J.S.R. (1993) ANFIS: Adaptive-Network based Fuzzy Inference System, IEEE transactions on Systems, Man, and Cybernetics, 23(3), pp. 665-685 https://doi.org/10.1109/21.256541
  11. Simon Haykin (1999) Neural Networks. 2nd Ed. Prentice Hall International, Inc
  12. Tay, J.H. and Zhang, X.(2000) A Fast Prediction Neural Fuzzy Model for High-rage Anaerobic Wastewater Treatment Systems, Water Resources Research, 34(11), pp. 2849-2860 https://doi.org/10.1016/S0043-1354(00)00057-9
  13. Mohammad N.A. and Jagath,J.K. (2005) Modular neural networks to predict the nitrate distribution in ground water using the on-ground nitrogen loading and recharge data, Entironmental modelling & software, 20(7), pp. 851-871 https://doi.org/10.1016/j.envsoft.2004.05.001