Scheduled System Maintenance:
On May 6th, single article purchases and IEEE account management will be unavailable from 8:00 AM - 5:00 PM ET (12:00 - 21:00 UTC). We apologize for the inconvenience.
By Topic

Groundwater Level Dynamic Prediction Based on Chaos Optimization and Support Vector Machine

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

3 Author(s)
Jin Liu ; Inst. of Water Resources & Hydroelectric Eng., Xi''an Univ. of Technol., Xi''an, China ; Jian-xia Chang ; Wen-ge Zhang

Groundwater level has random characters because of influences factors of natural and anthropogenic. Study random prediction model of groundwater level on the basis of groundwater physical process analysis is important to groundwater appraisal. The theory of supporting vector machine based on small-sample machine learning theory is introduced into dynamic prediction of groundwater level. A least square support vector machine groundwater level dynamic forecasting model based on chaos optimization peak value identification was proposed and applied in Hetao irrigation district in Inner Mongolia. The results show that the fitted values, the tested values and the predicted values of this model have little different from their real values. And they indicate that the model is feasible and effective. So the model proposed in this paper can provide a new tool for groundwater level dynamic forecasting.

Published in:

Genetic and Evolutionary Computing, 2009. WGEC '09. 3rd International Conference on

Date of Conference:

14-17 Oct. 2009