IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v21y2007i3p533-556.html
   My bibliography  Save this article

A neuro-fuzzy model for inflow forecasting of the Nile river at Aswan high dam

Author

Listed:
  • Ahmed El-Shafie
  • Mahmoud Taha
  • Aboelmagd Noureldin

Abstract

River flow forecasting is an essential procedure that is necessary for proper reservoir operation. Accurate forecasting results in good control of water availability, refined operation of reservoirs and improved hydropower generation. Therefore, it becomes crucial to develop forecasting models for river inflow. Several approaches have been proposed over the past few years based on stochastic modeling or artificial intelligence (AI) techniques. In this article, an adaptive neuro-fuzzy inference system (ANFIS) model is proposed to forecast the inflow for the Nile River at Aswan High Dam (AHD) on monthly basis. A major advantage of the fuzzy system is its ability to deal with imprecision and vagueness in inflow database. The ANFIS model divides the input space into fuzzy sub-spaces and maps the output using a set of linear functions. A historical database of monthly inflows at AHD recorded over the past 130 years is used to train the ANFIS model and test its performance. The performance of the ANFIS model is compared to a recently developed artificial neural networks (ANN) model. The results show that the ANFIS model was capable of providing higher inflow forecasting accuracy specially at extreme inflow events compared with that of the ANN model. It is concluded that the ANFIS model can be quite beneficial in water management of Lake Nasser reservoir at AHD. Copyright Springer Science+Business Media B.V. 2007

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:21:y:2007:i:3:p:533-556
    DOI: 10.1007/s11269-006-9027-1
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11269-006-9027-1
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11269-006-9027-1?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. Nozer D. Singpurwalla & Jane M. Booker, 2004. "Membership Functions and Probability Measures of Fuzzy Sets," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 867-877, January.
    Full references (including those not matched with items on IDEAS)

    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. Coletti, Giulianella & Gervasi, Osvaldo & Tasso, Sergio & Vantaggi, Barbara, 2012. "Generalized Bayesian inference in a fuzzy context: From theory to a virtual reality application," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 967-980.
    2. Nguyen, Hung T. & Wu, Berlin, 2006. "Random and fuzzy sets in coarse data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 70-85, November.
    3. Fausto Cavallaro & Edmundas Kazimieras Zavadskas & Saulius Raslanas, 2016. "Evaluation of Combined Heat and Power (CHP) Systems Using Fuzzy Shannon Entropy and Fuzzy TOPSIS," Sustainability, MDPI, vol. 8(6), pages 1-21, June.
    4. Giordani, Paolo, 2010. "Three-way analysis of imprecise data," Journal of Multivariate Analysis, Elsevier, vol. 101(3), pages 568-582, March.
    5. Coletti, Giulianella & Scozzafava, Romano, 2006. "Conditional probability and fuzzy information," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 115-132, November.
    6. Antonio Calcagnì & Luigi Lombardi, 2022. "Modeling random and non-random decision uncertainty in ratings data: a fuzzy beta model," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(1), pages 145-173, March.
    7. Wei Song & Zhiya Chen & Aijun Liu & Qiuyun Zhu & Wei Zhao & Sang-Bing Tsai & Hui Lu, 2018. "A Study on Green Supplier Selection in Dynamic Environment," Sustainability, MDPI, vol. 10(4), pages 1-22, April.
    8. Jay Verkuilen, 2005. "Assigning Membership in a Fuzzy Set Analysis," Sociological Methods & Research, , vol. 33(4), pages 462-496, May.
    9. Shapiro, Arnold F., 2013. "Modeling future lifetime as a fuzzy random variable," Insurance: Mathematics and Economics, Elsevier, vol. 53(3), pages 864-870.
    10. Hassanniakalager, Arman & Sermpinis, Georgios & Stasinakis, Charalampos & Verousis, Thanos, 2020. "A conditional fuzzy inference approach in forecasting," European Journal of Operational Research, Elsevier, vol. 283(1), pages 196-216.
    11. Zadeh, Lotfi A., 2006. "Generalized theory of uncertainty (GTU)--principal concepts and ideas," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 15-46, November.
    12. Pierpaolo D’Urso & María Ángeles Gil, 2017. "Fuzzy data analysis and classification," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 11(4), pages 645-657, December.
    13. Arnold Shapiro, 2013. "Fuzzy post-retirement financial concepts: an exploratory study," METRON, Springer;Sapienza Università di Roma, vol. 71(3), pages 261-278, November.
    14. Kimberly F. Sellers & Nozer D. Singpurwalla, 2008. "Many‐valued Logic in Multistate and Vague Stochastic Systems," International Statistical Review, International Statistical Institute, vol. 76(2), pages 247-267, August.
    15. Coppi, Renato & Gil, Maria A. & Kiers, Henk A.L., 2006. "The fuzzy approach to statistical analysis," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 1-14, November.
    16. Aven, T., 2011. "Interpretations of alternative uncertainty representations in a reliability and risk analysis context," Reliability Engineering and System Safety, Elsevier, vol. 96(3), pages 353-360.

    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:21:y:2007:i:3:p:533-556. 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.