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Modeling and Prediction of Great Salt Lake Elevation Time Series Based on ARFIMA

[+] Author Affiliations
Rongtao Sun

Phase Dynamics, Inc., Richardson, TX

YangQuan Chen, Qianru Li

Utah State University, Logan, UT

Paper No. DETC2007-34905, pp. 1349-1359; 11 pages
  • ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 5: 6th International Conference on Multibody Systems, Nonlinear Dynamics, and Control, Parts A, B, and C
  • Las Vegas, Nevada, USA, September 4–7, 2007
  • Conference Sponsors: Design Engineering Division and Computers and Information in Engineering Division
  • ISBN: 0-7918-4806-X | eISBN: 0-7918-3806-4
  • Copyright © 2007 by ASME


The elevation of Great Salt Lake (GSL) has a great impact on the people of Utah. The flood of GSL in 1982 has caused a loss of millions of dollars. Therefore, it is very important to predict the GSL levels as precisely as possible. This paper points out the reason why conventional methods failed to describe adequately the rise and fall of the GSL levels — the long-range dependence (LRD) property. The LRD of GSL elevation time series is characterized by some most commonly used Hurst parameter estimation methods in this paper. Then, according to the revealed LRD, the autoregressive fractional integrated moving average (ARFIMA) model is applied to analyze the data and predict the future levels. We have shown that the prediction results has a better performance compared to the conventional ARMA models.

Copyright © 2007 by ASME
Topics: Modeling , Time series , Lakes



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