By Topic

River Water Turbidity Forecasting Based on Phase Space Reconstruction and Support Vector Regression

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Wang Jun-dong ; Harbin Inst. of Technol., Harbin, China ; Li Pei-yan ; Zhang Yong-ming ; Qi Wei-gui

Due to the nonlinear and nonstationary of river water turbidity, a novel hybrid forecasting model based on phase space reconstruction and support vector regression (PSR-SVR) is proposed. Firstly, the embedding dimension is chosen by using the false nearest neighbor method, and the time delay is obtained by the average mutual information. The phase space is reconstructed from the time series with the embedding dimension and the time delay got. The reconstructed time array is used as the input signal of support vector regression network. Then the forecasting model is established. Utilizing the model to forecast the river water turbidity, and it shows the accuracy of this new forecasting model is superior to RBF and BP forecasting methods.

Published in:

Intelligent Computation Technology and Automation (ICICTA), 2010 International Conference on  (Volume:3 )

Date of Conference:

11-12 May 2010