Runoff Projection under Climate Change Conditions with Data-Mining Methods
Publication: Journal of Irrigation and Drainage Engineering
Volume 143, Issue 8
Abstract
This work proposes data-mining algorithms for runoff projection under climate change conditions. Specifically, genetic programming (GP), artificial neural network (ANN), and support vector machine (SVM) data-mining tools are applied for runoff projection and their predictive skills are compared by means of several standard indicators of models’ performance. The approach herein implemented predicts future regional precipitation and temperature with the Hadley Centre Coupled Atmosphere-Ocean General Circulation Model version 3 (HadCM3) atmosphere-ocean general circulation model (AOGCM) followed by runoff prediction with GP, ANN, and SVM in the Aidoghmoush Basin, Iran. This paper’s results demonstrate that SVM outperforms GP and ANN by 7 and 5%, respectively.
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Information
Published In
Journal of Irrigation and Drainage Engineering
Volume 143 • Issue 8 • August 2017
Copyright
©2017 American Society of Civil Engineers.
History
Received: Oct 21, 2016
Accepted: Feb 27, 2017
Published online: May 20, 2017
Published in print: Aug 1, 2017
Discussion open until: Oct 20, 2017
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