Date of Award:
5-2012
Document Type:
Dissertation
Degree Name:
Doctor of Philosophy (PhD)
Department:
Civil and Environmental Engineering
Committee Chair(s)
David K. Stevens
Committee
David K. Stevens
Committee
Thomas Hardy
Committee
Gilberto E. Urroz
Committee
John Schmidt
Committee
Michael J. McFarland
Abstract
The main objective of the research is the development of Multivariate Relevant Vector Machine (MVRVM) to predict suspended fine sediment, water quality constituents, and provide an understanding for the practical problem of determining the amount of data required for the MVRVM. MVRVM is a statistical learning algorithm that is based on Bayes theory. It has been widely used to predict patterns in hydrological systems and other fields. This research represents the first known attempt to use a MVRVM approach to predict transport of very fine sediment and water quality constituents in a complex natural system.
The results demonstrate the ability of the MVRVM to capture and predict the underlying patterns in data. Also careful construction of the experimental design for data collection can lead the Relevant Vectors (RVs is a subset of training observation which carry significant information that is used for prediction) to show locations of significant patterns.
Checksum
987ea0f3da3e8da78b1de04f39fa9bc5
Recommended Citation
Batt, Hussein Aly, "Using Relevance Vector Machines Approach for Prediction of Total Suspended Solids and Turbidity to Sustain Water Quality and Wildlife in Mud Lake" (2012). All Graduate Theses and Dissertations, Spring 1920 to Summer 2023. 1315.
https://digitalcommons.usu.edu/etd/1315
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Comments
This work made publicly available electronically on September 18, 2013.