IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v25y2011i3p963-985.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11269-010-9736-3
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11269-010-9736-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. Mendonca, Augusto F. & Dahl, Carol, 1999. "The Brazilian electrical system reform," Energy Policy, Elsevier, vol. 27(2), pages 73-83, February.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Ozgur Kisi & Alireza Nia & Mohsen Gosheh & Mohammad Tajabadi & Azadeh Ahmadi, 2012. "Intermittent Streamflow Forecasting by Using Several Data Driven Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(2), pages 457-474, January.
    3. 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.
    4. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wensheng Wang & Juliang Jin & Yueqing Li, 2009. "Prediction of Inflow at Three Gorges Dam in Yangtze River with Wavelet Network Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(13), pages 2791-2803, October.
    2. Kostić, Srđan & Stojković, Milan & Prohaska, Stevan, 2016. "Hydrological flow rate estimation using artificial neural networks: Model development and potential applications," Applied Mathematics and Computation, Elsevier, vol. 291(C), pages 373-385.
    3. Gnansounou, Edgard & Dong, Jun, 2004. "Opportunity for inter-regional integration of electricity markets: the case of Shandong and Shanghai in East China," Energy Policy, Elsevier, vol. 32(15), pages 1737-1751, October.
    4. Dalibor Petković & Milan Gocic & Slavisa Trajkovic & Miloš Milovančević & Dragoljub Šević, 2017. "Precipitation concentration index management by adaptive neuro-fuzzy methodology," Climatic Change, Springer, vol. 141(4), pages 655-669, April.
    5. Qiang Zhang & Ben-De Wang & Bin He & Yong Peng & Ming-Lei Ren, 2011. "Singular Spectrum Analysis and ARIMA Hybrid Model for Annual Runoff Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(11), pages 2683-2703, September.
    6. Mohammed Seyam & Faridah Othman, 2014. "The Influence of Accurate Lag Time Estimation on the Performance of Stream Flow Data-driven Based Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(9), pages 2583-2597, July.
    7. Jehangir Awan & Deg-Hyo Bae, 2014. "Improving ANFIS Based Model for Long-term Dam Inflow Prediction by Incorporating Monthly Rainfall Forecasts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(5), pages 1185-1199, March.
    8. Deepti Rani & Maria Moreira, 2010. "Simulation–Optimization Modeling: A Survey and Potential Application in Reservoir Systems Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(6), pages 1107-1138, April.
    9. Tolmasquim, Mauricio Tiomno & Cohen, Claude & Szklo, Alexandre Salem, 2001. "CO2 emissions in the Brazilian industrial sector according to the integrated energy planning model (IEPM)," Energy Policy, Elsevier, vol. 29(8), pages 641-651, June.
    10. Mendes, Carlos André B. & Beluco, Alexandre & Canales, Fausto Alfredo, 2017. "Some important uncertainties related to climate change in projections for the Brazilian hydropower expansion in the Amazon," Energy, Elsevier, vol. 141(C), pages 123-138.
    11. Yahia Mutalib Tofiq & Sarmad Dashti Latif & Ali Najah Ahmed & Pavitra Kumar & Ahmed El-Shafie, 2022. "Optimized Model Inputs Selections for Enhancing River Streamflow Forecasting Accuracy Using Different Artificial Intelligence Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(15), pages 5999-6016, December.
    12. Corton, Maria Luisa & Zimmermann, Aneliese & Phillips, Michelle Andrea, 2016. "The low cost of quality improvements in the electricity distribution sector of Brazil," Energy Policy, Elsevier, vol. 97(C), pages 485-493.
    13. Ahmad Khazaee Poul & Mojtaba Shourian & Hadi Ebrahimi, 2019. "A Comparative Study of MLR, KNN, ANN and ANFIS Models with Wavelet Transform in Monthly Stream Flow Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(8), pages 2907-2923, June.
    14. Resende, Marcelo & Cardoso, Vicente, 2019. "Mapping service quality in electricity distribution: An exploratory study of Brazil," Utilities Policy, Elsevier, vol. 56(C), pages 41-52.
    15. Haselip, James & Dyner, Isaac & Cherni, Judith, 2005. "Electricity market reform in Argentina: assessing the impact for the poor in Buenos Aires," Utilities Policy, Elsevier, vol. 13(1), pages 1-14, March.
    16. Muhammad Sulaiman & Ahmed El-Shafie & Othman Karim & Hassan Basri, 2011. "Improved Water Level Forecasting Performance by Using Optimal Steepness Coefficients in an Artificial Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(10), pages 2525-2541, August.
    17. Hadi Sanikhani & Ozgur Kisi & Mohammad Nikpour & Yagob Dinpashoh, 2012. "Estimation of Daily Pan Evaporation Using Two Different Adaptive Neuro-Fuzzy Computing Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(15), pages 4347-4365, December.
    18. Viana, Alexandre G. & Ramos, Dorel Soares, 2018. "Outcomes from the first large-scale solar PV auction in Brazil," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 219-228.
    19. Ma, Chunbo & He, Lining, 2008. "From state monopoly to renewable portfolio: Restructuring China's electric utility," Energy Policy, Elsevier, vol. 36(5), pages 1697-1711, May.
    20. Reza Mohammadpour & Aminuddin Ghani & Mohammadtaghi Vakili & Tooraj Sabzevari, 2016. "Prediction of temporal scour hazard at bridge abutment," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 80(3), pages 1891-1911, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:waterr:v:25:y:2011:i:3:p:963-985. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.