Application of Artificial Neural Networks to Forecasting Ice Conditions of the Yellow River in the Inner Mongolia Reach
Publication: Journal of Hydrologic Engineering
Volume 13, Issue 9
Abstract
Ice condition forecasts are very important for preventing ice disasters. Because of the complexity of ice conditions, traditional methods could hardly give accurate prediction in the ice condition forecast, especially for the meandering rivers such as the Yellow River, while the artificial neural networks (ANNs) have an obvious advantage over other traditional methods for forecasting ice conditions. An ANN model based on feed-forward back-propagation and improved by the Levenberg-Marquardt algorithm is applied to forecast the ice conditions of the Yellow River in the Inner Mongolia region. The forecast results in the winter of 2004–2005 are in good agreement with the measured ones. Simulation also shows that the ANN model is superior to the multiple linear regression model and the GM (0,1) model.
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Acknowledgments
The writers would like to thank the NNSFCNational Nature Science Foundation of China and Hydrology Bureau of Yellow River Conservancy Commission for their support during this study. A further study is under way.
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Published In
Journal of Hydrologic Engineering
Volume 13 • Issue 9 • September 2008
Pages: 811 - 816
Copyright
© 2008 ASCE.
History
Received: Aug 11, 2006
Accepted: Nov 5, 2007
Published online: Sep 1, 2008
Published in print: Sep 2008
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