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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
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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. 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.

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