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

John Flake, Todd K. Moon, Mac McKee and Jacob H. Gunther

Agricultural Water Management, 2010, vol. 97, issue 2, 208-214

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.

Keywords: Water; demand; Irrigation; canals; Flow; rate; modeling; Forecasting; Supervised; machine; learning (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:agiwat:v:97:y:2010:i:2:p:208-214

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