Ice-Jam Forecasting during River Breakup Based on Neural Network Theory
Publication: Journal of Cold Regions Engineering
Volume 32, Issue 3
Abstract
Forecasting of ice jams and their breakup is crucial to prevent or reduce flooding risk in cold regions. A back propagation (BP) neural network model improved by the Levenberg-Marquardt clustering method has been developed with air temperatures and precipitation as inputs and applied for ice-jam forecasting in a given year in the upper reaches of the Heilongjiang River (Amur River), where ice flooding occurs frequently during spring. The accuracy rate achieved was 85%, higher than that obtained using the conventional statistical method (62% accuracy), for ice-jam breakup forecasting. The BP model has a forecast period of 10 days with a maximum error of two days and a qualified rate of 100% for national standards breakup date forecasting. The forecast on the ice-jam breakup, which was released 24 days ahead, provided accurate results for the breakup date and the occurrence of ice jams in the spring of 2017.
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Acknowledgments
The authors are grateful for the funding support provided by the National Key Research & Development Plan of China (2017YFC0405103, 2017YFC0405704), IWHR Research & Development Support Program (HY0145B642017, SKL2017CGS04, HY0145B912017), the Special Scientific Research Fund of Public Welfare Profession of China (201501025, 201301032) and the National Natural Science Foundation of China (51679263). The authors also greatly appreciate the support from the Heilongjiang Hydrologic Bureau and the National Meteorological Administration in providing hydrological and meteorological data.
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Published In
Journal of Cold Regions Engineering
Volume 32 • Issue 3 • September 2018
Copyright
©2018 American Society of Civil Engineers.
History
Received: Sep 25, 2017
Accepted: May 9, 2018
Published online: Jul 13, 2018
Published in print: Sep 1, 2018
Discussion open until: Dec 13, 2018
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