This work addresses management of the scarce water resource 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, this work employs a data-driven approach to developing canal flow prediction models using the Relevance Vector Machine (RVM), a probabilistic kernel-based learning machine. Beyond the RVM learning process, which establishes the set of relevant vectors from the training data, a search is performed across model attributes including input set, kernel scale parameter, and model update scheme for models providing superior prediction capability. Models are developed for two canals in the Sevier River Basin of southern Utah for prediction horizons of up to five days. Appendices provide the RVM derivation in detail.
| Identifer | oai:union.ndltd.org:UTAHS/oai:digitalcommons.usu.edu:etd-1265 |
| Date | 01 May 2007 |
| Creators | Flake, John T. |
| Publisher | DigitalCommons@USU |
| Source Sets | Utah State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | All Graduate Theses and Dissertations |
| Rights | Copyright for this work is held by the author. Transmission or reproduction of materials protected by copyright beyond that allowed by fair use requires the written permission of the copyright owners. Works not in the public domain cannot be commercially exploited without permission of the copyright owner. Responsibility for any use rests exclusively with the user. For more information contact Andrew Wesolek (andrew.wesolek@usu.edu). |
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