Luiz Biondi Neto, Pedro Henrique Gouvêa Coelho, Maria Luiza Fernandes Velloso, João Carlos C. B. Soares de Mello, Lidia Angulo Meza


This paper investigates the application of partially recurrent artificial neural networks (ANN) in the flow estimation for São Francisco River that feeds the hydroelectric power plant of Sobradinho. An Elman neural network was used suitably arranged to receive samples of the flow time series data available for São Francisco River shifted by one month. For that, the neural network input had a delay loop that included several sets of inputs separated in periods of five years monthly shifted. The considered neural network had three hidden layers. There is a feedback between the output and the input of the first hidden layer that enables the neural network to present temporal capabilities useful in tracking time variations. The data used in the application concern to the measured São Francisco river flow time series from 1931 to 1996, in a total of 65 years from what 60 were used for training and 5 for testing. The obtained results indicate that the Elman neural network is suitable to estimate the river flow for 5 year periods monthly. The average estimation error was less than 0.2 %.


  1. Chatfield E., 1991. The Analysis of Time Series, New York, USA, Chapman and Hall, fourth edition.
  2. Moraes, J. M., Pellegrino, G., Ballester, M. V., Martinelli, L. A., Victoria, R. L., 1995. Hydrological Parameters of a Southern Brazilian Watershed and its Relation to Human Induced Changes. In Annales of 20th General Assembly of the European Geophyscial Society, v13: 506-507.
  3. Moraes, J. M., Genovez, A.M., Mortatti, J., Pellegrino,G.,Ballester, M.V., Martinelli, L. A, 1996. Analyses and Modelling of a Flow Time Series under the Influence of Man Made and Natural Actions. In Anais do XVII Congresso Latino Americano de Hidraúlica. (in Portuguese)
  4. Tucci, C. E. M., Robin T. C., Dias P. L. da S., Collischonn W., 2002. Medium Run Prediction of Reservoirs Flows based on Weather Forecast. In Final Project Report:BRA/00/029, Instituto de Astronomia, Geofísica e Ciências Atmosféricas Universidade de São Paulo and Instituto de Pesquisas Hidráulicas Universidade Federal do Rio Grande do Sul. (in Portuguese)
  5. Box, G. E. P., and Jenkins, G. M., 1976. Time Series Analysis: Forecasting and Control. California, USA. San Francisco: Holden Day, 2nd. ed.
  6. Hoff C. J., 1983. A Practical Guide to Box-Jenkins Forecasting, Belmont, CA., USA, Lifetime Learning Publications.
  7. Fog, T.L. et al, 1995. Training and Evaluation of Neural Networks for Multi-Variate Time Series Processing. In Proceedings of IEEE International Conference on Neural Networks. IEEE Press.
  8. Lachtermacher, G. and Fuller, J.D., 1995. Backpropagation in Time series Forecasting. In Journal of Forecasting. Vol 14, 381-393.
  9. Sarle, W.S., 1995. Stopped Training and other remedies for Overfitting. In Proceedings of the 27th Symposium on the Interface.
  10. Haykin, S. Neural Networks : A Comprehensive Foundation, Prentice-Hall, New Jersey, 1999.
  11. Evans, R. M. and S. Alvin, J., 1991. Relating Numbers of Processing Elements in a Sparse Distributed Memory Model to Learning Rate and Generalization, In ACM APL Quote Quad, v21(4), 166-173.
  12. Siqueira, T. G., Soares Filho, Secundino, 2002. Application of Neural Networks with Radial Basis Activation Function to the prediction of Nonstationary Time Series, In XIV Congresso Brasileiro de Automação. (in Portuguese)
  13. Elman J. L., Finding Structure in Time, 1990. In Cognitive Science, vol. 14, pp. 179-211.
  14. Jacek M. Zurada and Tomasz J. Cholewo, 1997. Sequential Network Construction for Time Series Prediction. In Proceedings of the IEEE International Joint Conference on Neural Networks, pp 2034-2039.
  15. Ronaldo R. B. de Aquino, Manoel Afonso de Carvalho Jr., Benemar Alencar de Souza, 1999. Artificial Neural Networks: An Application to the Operation Planning of Hydrothermal Generation Systems. In Proceedings of the IV Brazilian Conference on Neural Networks - IV Congresso Brasileiro de Redes Neurais, pp. 164-169. (in portuguese)
  16. Armando Zeferino Milioni e Acioli Antonio de Olivo, 2001. Econometric Models for the Forecast of River Floods . In Simpósio Brasileiro de Pesquisa Operacional. (in portuguese)
  17. Tucci, C. E. M., Brun, Gerti, 2001. Real Time Forecast of the Volume Flowing to the Reservoir of Ernestina. In Revista Brasileira de Recursos Hídricos. v.6, n.2, p.73 - 79. (in Portuguese)
  18. Cichocki, A., Unbehauen, R., 1996. Neural Networks for Optimisation and Signal Processing, New York, USA, John Wiley & Sons, Inc.

Paper Citation

in Harvard Style

Biondi Neto L., Henrique Gouvêa Coelho P., Luiza Fernandes Velloso M., Carlos C. B. Soares de Mello J. and Angulo Meza L. (2004). MONTHLY FLOW ESTIMATION USING ELMAN NEURAL NETWORKS . In Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 972-8865-00-7, pages 153-158. DOI: 10.5220/0002610101530158

in Bibtex Style

author={Luiz Biondi Neto and Pedro Henrique Gouvêa Coelho and Maria Luiza Fernandes Velloso and João Carlos C. B. Soares de Mello and Lidia Angulo Meza},
booktitle={Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 2: ICEIS,},

in EndNote Style

JO - Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 2: ICEIS,
SN - 972-8865-00-7
AU - Biondi Neto L.
AU - Henrique Gouvêa Coelho P.
AU - Luiza Fernandes Velloso M.
AU - Carlos C. B. Soares de Mello J.
AU - Angulo Meza L.
PY - 2004
SP - 153
EP - 158
DO - 10.5220/0002610101530158