GA-Based Support Vector Machine Model for the Prediction of Monthly Reservoir Storage
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Volume 19, Issue 7
Abstract
Reservoir storage prediction is essential to the operation and management of reservoirs. In this paper, a genetic algorithm (GA)-based support vector machine (SVM) model was developed for the prediction of monthly reservoir storage of Miyun Reservoir (the only surface drinking water source for Beijing city) over the period of 1995 to 2011. At the same time, two other SVM-based models that combine grid search and particle swarm optimization methods respectively for the parameter optimization, were used for comparison. The results showed that the developed GA-SVM model had the best performance in calibration and prediction. Owing to its high accuracy, the GA-SVM model would be popularized to the prediction of reservoir storage in other regions.
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Acknowledgments
This research was supported by National Science Foundation for Innovative Research Group (No. 51121003), National Water Pollution Control Major Project of China (No. 2008ZX07209–009), and the National Science and Technology Support Program (No. 2011BAC12B02). Thanks to the Institute of Geographic Sciences and Natural Resources Research, CAS for providing the free geospatial data.
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Published In
Journal of Hydrologic Engineering
Volume 19 • Issue 7 • July 2014
Pages: 1430 - 1437
Copyright
© 2013 American Society of Civil Engineers.
History
Received: May 26, 2013
Accepted: Sep 27, 2013
Published online: Sep 30, 2013
Discussion open until: Feb 28, 2014
Published in print: Jul 1, 2014
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