IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v97y2010i2p208-214.html
   My bibliography  Save this article

Application of the relevance vector machine to canal flow prediction in the Sevier River Basin

Author

Listed:
  • Flake, John
  • Moon, Todd K.
  • McKee, Mac
  • Gunther, Jacob H.

Abstract

This work addresses management of water for irrigation in arid regions where significant delays between the time of order and the time of delivery present major difficulties. Motivated by improvements to water management that will be facilitated by an ability to predict water demand, it employs a data-driven approach to developing canal flow prediction models using the relevance vector machine (RVM), a probabilistic kernel-based learning machine. A search is performed across model attributes including input set, kernel scale parameter and model update scheme for models providing superior prediction capability using the RVM. Models are developed for two canals in the Sevier River Basin of southern Utah for prediction horizons of up to 5 days.

Suggested Citation

  • Flake, John & Moon, Todd K. & McKee, Mac & Gunther, Jacob H., 2010. "Application of the relevance vector machine to canal flow prediction in the Sevier River Basin," Agricultural Water Management, Elsevier, vol. 97(2), pages 208-214, February.
    • Handle: RePEc:eee:agiwat:v:97:y:2010:i:2:p:208-214
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378-3774(09)00279-0
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

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

    Citations

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


    Cited by:

    1. Sen Guo & Haoran Zhao & Huiru Zhao, 2017. "A New Hybrid Wind Power Forecaster Using the Beveridge-Nelson Decomposition Method and a Relevance Vector Machine Optimized by the Ant Lion Optimizer," Energies, MDPI, vol. 10(7), pages 1-20, July.
    2. Ding, Jia & Wang, Maolin & Ping, Zuowei & Fu, Dongfei & Vassiliadis, Vassilios S., 2020. "An integrated method based on relevance vector machine for short-term load forecasting," European Journal of Operational Research, Elsevier, vol. 287(2), pages 497-510.
    3. Lecina, S. & Neale, C.M.U. & Merkley, G.P. & Dos Santos, C.A.C., 2011. "Irrigation evaluation based on performance analysis and water accounting at the Bear River Irrigation Project (U.S.A.)," Agricultural Water Management, Elsevier, vol. 98(9), pages 1349-1363, July.

    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:eee:agiwat:v:97:y:2010:i:2:p:208-214. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/agwat .

    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.