By Topic

Using two-stage genetic algorithms to solve the nonlinear time series models for ten-day streamflow forecasting

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
$33 $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

2 Author(s)
Chin-Hui Liu ; Department of Graduate Institute of Civil and Hydraulic Engineering, Feng Chia University, Taichung, Taiwan ; Chang-Shian Chen

Streamflow forecasting is of utmost importance for the management of water resources. A higher accuracy in flow prediction can lead to a more effective and comprehensive application of water resources. The characteristics of hydrological data can be classified as non-steady and nonlinear. This study used two-stage genetic algorithms to solve complex nonlinear time series models. Ten-day streamflows of the Wu-shi river in Taiwan were taken as an example. Compared with the traditional linear time series, the analysis verified that nonlinear time series models by two-stage genetic algorithms are superior.

Published in:

2007 IEEE Congress on Evolutionary Computation

Date of Conference:

25-28 Sept. 2007