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Application of Neural Network Model for Ice Jam Forecasting in the Heilongjiang River

Author(s): Xinlei Guo; Tao Wang; Hui Fu; Yongxin Guo; Li Jiazheng

Linked Author(s): Xinlei Guo, Tao Wang

Keywords: River ice; Breakup; Ice jam; Forecast; Neural network; Heilongjiang River

Abstract: The forecast of ice jams and its break-up is crucial to prevent possible flooding in cold region. Current breakup ice jam forecasting methods are largely based on conventional statistical analyses or past experience. New forecasting technologies are urgently needed for ice flood management. In this paper, an ice jam forecasting model was established based on the neural network theory and used for ice jam forecasting in the upper Heilongjiang River (Amur River), where ice floods occur frequently. The model based on the neural network clustering method had an accuracy rate of 85%, which was significantly higher than the 62% accuracy rate of the conventional statistical method for breakup ice jam forecasting. The model had a forecast period of 10 days with a maximum error of 2 days and forecast qualified rate of 100% for breakup date forecasting. The forecast on the breakup ice jam, which was released 24 days ahead, provides the accurate results for the breakup date and the occurrence of breakup ice jams in the spring of 2017.


Year: 2018

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