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Water engineering research : international journal of KWRA v.3 no.2, 2002년, pp.143 - 153   피인용횟수: 1

DEVELOPMENT OF ARTIFICIAL NEURAL NETWORK MODELS SUPPORTING RESERVOIR OPERATION FOR THE CONTROL OF DOWNSTREAM WATER QUALITY

Chung, Se-Woong   (Water Resources Operations Center, KOWACO  ); Kim, Ju-Hwan   (Water Research Institute, KOWACO  );
  • 초록

    As the natural flows in rivers dramatically decrease during drought season in Korea, a deterioration of river water quality is accelerated. Thus, consideration of downstream water quality responding to changes in reservoir release is essential for an integrated watershed management with regards to water quantity and quality. In this study, water quality models based on artificial neural networks (ANNs) method were developed using historical downstream water quality (rm $\NH_3$ -N) data obtained from a water treatment plant in Geum river and reservoir release data from Daechung dam. A nonlinear multiple regression model was developed and compared with the ANN models. In the models, the rm NH $_3$ -N concentration for next time step is dependent on dam outflow, river water quality data such as pH, alkalinity, temperature, and rm $\NH_3$ -N of previous time step. The model parameters were estimated using monthly data from Jan. 1993 to Dec. 1998, then another set of monthly data between Jan. 1999 and Dec. 2000 were used for verification. The predictive performance of the models was evaluated by comparing the statistical characteristics of predicted data with those of observed data. According to the results, the ANN models showed a better performance than the regression model in the applied cases.


  • 주제어

    water quality .   drought season .   dam outflow .   artificial neural network.  

  • 참고문헌 (9)

    1. Roman, H. and Sunilkumar, N. (1995). 'Multivariate modeling of water resources time series using artificial neural networks.' Hydrological Sci. J., 40(2), pp. 145-163 
    2. Shamseldin, A.Y. (1997). 'Application of a neural network technique to rainfall-runoff modeling.' J. Hydro., Amsterdam, 199, pp. 272-294 
    3. Zealand, C.M., Burn, D.H., and Simonovic, S.P. (1999). 'Short term streamflow forecasting using artificial neural networks.' J. Hydro., Amsterdam, 214, pp. 32-48 
    4. Lisboa, P.G.J. (1992). Neural networks. Chapman & hall, London, pp. 5-6 
    5. Karunanithi, N., Grenney, W.J., Whitley, D., and Bovee, K. (1994). 'Neural networks for river flow prediction.' J. Comp. in Civ. Engrg., ASCE, 8(2), pp. 201-220 
    6. Kim, J.H., Kang, K.W., and Park, C.Y. (1992). 'Nonlinear forecasting of streamflows by pattern recognition method.' Korean J. of Hydroscience, Korean Ed., Vol. 25, No. 3, pp. 105-113 
    7. Kim, J.H. (1993). A study on hydrological forecasting of streamflows by using artificial neural network. Ph. D. Dissertation, Dept. of Civil Engrg., Inha Univ., Korea 
    8. Hsu, K.L., Gupta, H.V., and Sorooshian, S. (1995). 'Artificial neural network modeling of the rainfall-runoff process.' Water Resources Research, 31(10), pp. 2517-2530 
    9. French, M.N. Krajewski, W.F., and Cuykendall, R.R. (1992). 'Rainfall forecasting in space and time using a neural network.' J. of Hydrology, 137, pp. 1-31 
  • 이 논문을 인용한 문헌 (1)

    1. Chug, Se-Woong 2003. "Development of Multiple Regression Models for the Prediction of Daily Ammonia Nitrogen Concentrations" 韓國水資源學會論文集 = Journal of Korea Water Resources Association, 36(6): 1047~1058     

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