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Long-Term Runoff Modeling Using Rainfall Forecasts with Application to the Iguaçu River Basin

Author

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  • Alexandre Evsukoff
  • Beatriz Lima
  • Nelson Ebecken

Abstract

This work presents the development of a rainfall-runoff model for the Iguaçu River basin in southern Brazil. The model was developed to support the operation planning of hydroelectric power plants and is intended to predict the natural flow based on meteorological rain forecasts. A recurrent fuzzy system model was employed with parameters estimated by a genetic algorithm using observed rainfall as input. The model performs well using observed rainfall as input; however, its performance using predicted rainfall as input decays with the forecasting horizon, illustrating the effect of meteorological prediction errors. The prototype implementing the model has been used for dispatch planning by the Brazilian Electric System Operator. Copyright Springer Science+Business Media B.V. 2011

Suggested Citation

  • Alexandre Evsukoff & Beatriz Lima & Nelson Ebecken, 2011. "Long-Term Runoff Modeling Using Rainfall Forecasts with Application to the Iguaçu River Basin," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(3), pages 963-985, February.
    • Handle: RePEc:spr:waterr:v:25:y:2011:i:3:p:963-985
    DOI: 10.1007/s11269-010-9736-3
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    References listed on IDEAS

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    1. Muhammad Aqil & Ichiro Kita & Akira Yano & Soichi Nishiyama, 2007. "Neural Networks for Real Time Catchment Flow Modeling and Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(10), pages 1781-1796, October.
    2. Ahmed El-Shafie & Mahmoud Taha & Aboelmagd Noureldin, 2007. "A neuro-fuzzy model for inflow forecasting of the Nile river at Aswan high dam," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(3), pages 533-556, March.
    3. Mendonca, Augusto F. & Dahl, Carol, 1999. "The Brazilian electrical system reform," Energy Policy, Elsevier, vol. 27(2), pages 73-83, February.
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    Cited by:

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    2. José P. Matos & Maria M. Portela & Anton J. Schleiss, 2018. "Towards Safer Data-Driven Forecasting of Extreme Streamflows," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(2), pages 701-720, January.
    3. M. Mustafa & R. Rezaur & S. Saiedi & M. Isa, 2012. "River Suspended Sediment Prediction Using Various Multilayer Perceptron Neural Network Training Algorithms—A Case Study in Malaysia," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(7), pages 1879-1897, May.
    4. Pao-Shan Yu & Tao-Chang Yang & Chen-Min Kuo & Yi-Tai Wang, 2014. "A Stochastic Approach for Seasonal Water-Shortage Probability Forecasting Based on Seasonal Weather Outlook," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(12), pages 3905-3920, September.

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