Date of Award:
Master of Science (MS)
Electrical and Computer Engineering
Todd K. Moon
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.
Flake, John T., "Application of the Relevance Vector Machine to Canal Flow Prediction in the Sevier River Basin" (2007). All Graduate Theses and Dissertations. Paper 272.
Copyright for this work is retained by the student.