Long-Term Prediction of Discharges in Manwan Reservoir Using Artificial Neural Network Models

  • Chuntian Cheng
  • Kwokwing Chau
  • Yingguang Sun
  • Jianyi Lin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3498)


Several artificial neural network (ANN) models with a feed-forward, back-propagation network structure and various training algorithms, are developed to forecast daily and monthly river flow discharges in Manwan Reservoir. In order to test the applicability of these models, they are compared with a conventional time series flow prediction model. Results indicate that the ANN models provide better accuracy in forecasting river flow than does the auto-regression time series model. In particular, the scaled conjugate gradient algorithm furnishes the highest correlation coefficient and the smallest root mean square error. This ANN model is finally employed in the advanced water resource project of Yunnan Power Group.


Root Mean Square Error Artificial Neural Network Artificial Neural Network Model Time Series Model Normalize Root Mean Square Error 
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  1. 1.
    Box, G.E.P., Jenkins, G.M.: Time Series Analysis Forecasting and Control. Holden-Day, San Francisco (1976)zbMATHGoogle Scholar
  2. 2.
    ASCE Task Committee: Artificial Neural Networks in Hydrology-I: Preliminary Concepts. Journal of Hydrologic Engineering, ASCE 5, 115–123 (2000)CrossRefGoogle Scholar
  3. 3.
    ASCE Task Committee: Artificial Neural Networks in Hydrology-II: Hydrological Applications. Journal of Hydrologic Engineering, ASCE 5, 124–137 (2000)CrossRefGoogle Scholar
  4. 4.
    Haykin, S.: Neural Networks, a Comprehensive Foundation. Prentice Hall, Upper Saddle River (1999)zbMATHGoogle Scholar
  5. 5.
    Hagan, M.T., Demuth, H.B., Beale, M.: Neural Network Design. PWS Pub., Boston London (1996)Google Scholar
  6. 6.
    Fitch, J.P., Lehman, S.K., Dowla, F.U., Lu, S.K., Johansson, E.M., Goodman, D.M.: Ship Wake Detection Procedure Using Conjugate Gradient Trained Artificial Neural Network. IEEE Transactions on Geosciences and Remote Sensing 9, 718–725 (1991)CrossRefGoogle Scholar
  7. 7.
    Moller, M.F.: A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning. Neural Networks 6, 523–533 (1993)CrossRefGoogle Scholar
  8. 8.
    Huang, W., Foo, S.: Neural Network Modeling of Salinity in Apalachicola River. Water Resources Research 31, 2517–2530 (2002)Google Scholar
  9. 9.
    Wang, B.D.: Fuzzy Mathematical Methods for Long-Term Hydrological Prediction. Dalian University of Technology Press, DalianGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Chuntian Cheng
    • 1
  • Kwokwing Chau
    • 2
  • Yingguang Sun
    • 1
  • Jianyi Lin
    • 1
  1. 1.Institute of Hydroinformatics, Department of Civil EngineeringDalian University of TechnologyDalianChina
  2. 2.Department of Civil and Structural EngineeringHong Kong Polytechnic UniversityHong KongChina

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