Identification of Influential Climate Indicators, Prediction of Long-Term Streamflow and Great Salt Lake Elevation Using Machine Learning Approach

Author

Niroj K. Shrestha, Utah State University

Date of Award:

5-2012

Document Type:

Dissertation

Degree Name:

Doctor of Philosophy (PhD)

Department:

Civil and Environmental Engineering

Committee Chair(s)

Gilberto Urroz

Committee

Gilberto Urroz

Committee

Mac McKee

Committee

A. Bruce Bishop

Committee

David K. Stevens

Committee

YangQuan Chen

Abstract

In order to meet rising water demand due to rapid population growth and changing climatic conditions around the world, and to reduce the impact of floods and draughts, a comprehensive water management and planning is necessary. Water resource management requires the prediction of streamflow under climatic variability, and variability of hydrologic quantities that changes in time and space. Prediction of streamflow using physically-based model are usually complex and typically requires detailed knowledge of physical processes. The availability of data is an important issue to justify the use of these models. Using a data-driven model that relies on the machine learning approach, it is possible to produce reasonable predictions from a limited data set and limited knowledge of underlying physical processes of the system by just relating input and output. This dissertation uses the Multivariate Relevance Vector Machine (MVRVM) for identifying influential climate indicators, and uses them for long-term streamflow prediction for multiple lead times at different locations in Utah. Both MVRVM and Support Vector Machine (SVM) are used for prediction of Great Salt Lake (GSL) elevation series. They provide reasonable predictions of hydrological quantities from the available data. The predictions from these models are robust and parsimonious. The approach presented herein has potential value for water resources planning and management.

Checksum

81a30487695732911eaec5a2f7e8a215

Comments

This work made publicly available electronically on September 20, 2012.

Recommended Citation

Shrestha, Niroj K., "Identification of Influential Climate Indicators, Prediction of Long-Term Streamflow and Great Salt Lake Elevation Using Machine Learning Approach" (2012). All Graduate Theses and Dissertations, Spring 1920 to Summer 2023. 1335.
https://digitalcommons.usu.edu/etd/1335