Date of Award:
5-2007
Document Type:
Thesis
Degree Name:
Master of Science (MS)
Department:
Electrical and Computer Engineering
Committee Chair(s)
Todd K. Moon
Committee
Todd K. Moon
Committee
Jacob H. Gunther
Committee
YangQuan Chen
Abstract
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.
Checksum
a1c1b4487dbb350b86dd392f467618ac
Recommended Citation
Flake, John T., "Application of the Relevance Vector Machine to Canal Flow Prediction in the Sevier River Basin" (2007). All Graduate Theses and Dissertations, Spring 1920 to Summer 2023. 272.
https://digitalcommons.usu.edu/etd/272
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