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http://dx.doi.org/10.3741/JKWRA.2005.38.7.565

A Development of Real Time Artificial Intelligence Warning System Linked Discharge and Water Quality (I) Application of Discharge-Water Quality Forecasting Model  

Yeon, In-Sung (Dept. of Civil Eng., Chungbuk National Univ.)
Ahn, Sang-Jin (Dept. of Civil Eng., Chungbuk National Univ.)
Publication Information
Journal of Korea Water Resources Association / v.38, no.7, 2005 , pp. 565-574 More about this Journal
Abstract
It is used water quality data that was measured at Pyeongchanggang real time monitoring stations in Namhan river. These characteristics were analyzed with the water qualify of rainy and nonrainy periods. TOC (Total Organic Carbon) data of rainy periods has correlation with discharge and shows high values of mean, maximum, and standard deviation. DO (Dissolved Oxygen) value of rainy periods is lower than those of 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 qualify forecasting models were applied. LMNN, MDNN, and ANFIS models have achieved the highest overall accuracy of TOC data. LMNN (Levenberg-Marquardt Neural Network) and MDNN (MoDular Neural Network) model which are applied for DO forecasting shows better results than ANFIS (Adaptive Neuro-Fuzzy Inference System). MDNN model shows the lowest estimation error when using daily time, which is qualitative data trained with quantitative data. The observation of discharge and water quality are effective at same point as well as same time for real time management. But there are some of real time water quality monitoring stations far from the T/M water stage. Pyeongchanggang station is one of them. So discharge on Pyeongchanggang station was calculated by developed runoff neural network model, and the water quality forecasting model is linked to the runoff forecasting model. That linked model shows the improvement of waterquality forecasting.
Keywords
neural network; neuro-fuzzy; discharge; water quality; forecasting;
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Times Cited By KSCI : 5  (Citation Analysis)
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1 Tay, J.H. and Zhang, X. (2000). 'A fast prediction neural fuzzy model for high-rage anaerobic wastewater treatment systems.' Water Resources Research, Vol. 34, No. 11, pp. 2849-2860   DOI   ScienceOn
2 McMichael, F.C. and Hunter, Y.S. (1972). 'Stochastic modeling of temperature and flow in rivers.' Water Resources Research, Vol. 8, No. 1, pp. 87-98   DOI
3 Nouh, M. (1996). 'Simulation of Water Quality in Sewer Flowd by Neural Networks.' Hydro-informatics '96, Proceedings of the 2nd International Conference on Hydro-informatics, Zurich, Switzerland, pp. 885-891
4 연인성(2005). 실시간 유량-수질관리를 위한 인공지능 시스템 개발. 박사학위논문, 충북대학교
5 정상만, 임경호, 최정현(2000). '금강지류 유역에서의 유출량과 오염부하량의 상관관계 분석.' 한국수자원학회 논문집, 제33권, 제5호, pp. 527-536   과학기술학회마을
6 신현석, 최시중, 김중훈 (1998). '신경망을 이용한 도시유역 유출 및 비점원 오염물 배출 모형화 연구.' 대한토목학회논문집, 제18권, 제5-B호, pp. 437-448
7 Maier, H.R. and Dandy, G.C. (1996). 'The use of artificial neural networks for the prediction of water quality parameters.' Water Resources Research, Vol. 32, No. 4, pp. 1013-1022   DOI
8 Ken-ichi, Y., Masaaki, H. and Akihiho, M. (1997). 'Novel Application of a Back-propagation Artificial Neural Network Model Formulated to Predict Algal Bloom.' Water Science and Technology, Vol. 36, pp. 89-97   DOI   ScienceOn
9 Hahn, R.L. (1972). Time series analysis of daily measurements of water quality parameters of the passaic river at little falls, new Jersey. M.S. Thesis, Rutgers Univ., Nen Brunswick, N.J.
10 류병로, 한양수 (1998). 'ARIMA 모형에 의한 하천수질 예측.' 한국환경과학회지, 제7권, 제4호, pp. 433-440   과학기술학회마을
11 안상진, 최병만, 연인성, 곽현구 (2005). '미계측 지점에서의 유출 모의 및 예측.' 한국수자원학회 논문집, 제38권, 제6호, pp. 485-494   과학기술학회마을   DOI   ScienceOn
12 Zhang, B. and Govindaraju, R.S. (1998). 'Using Modular Neural Networks to Predict Watershed Runoff.' Water Resource Engineering Conference, ASCE, Vol. 1, pp. 897-902
13 Jang, J.S.R. (1993). 'ANFIS: Adaptive-Network based Fuzzy Inference System' IEEE transactions on Systems, Man, and Cybernetics, Vol. 23, No.3, pp. 665-685   DOI   ScienceOn
14 강관원, 박찬영, 김주환 (1992). '패턴인식방법을 적용한 하천유출의 비선형 예측.' 한국수문학회지, 제25권, 제3호, pp. 105-113   과학기술학회마을
15 김만식, 이요상, 심규철, 심순보 (2001). '신경망모형을 이용한 하천의 수질예측 연구.' 한국수자원학회 학술발표회 논문집, pp. 925-930   과학기술학회마을
16 Karunanithi, N. (1994) 'Neural Networks for River Flow Prediction.' Journal of Computing in Civil Engineering, ASCE, Vol. 8, No. 2, pp. 201-219   DOI   ScienceOn
17 Thomann, R.V. (1976). 'Time series analysis of water quality data.' ASCE, Vol. 93, No. 1, pp. 1-23