@article{ref1,
title="Ice-jam forecasting during river breakup based on neural network theory",
journal="Journal of cold regions engineering",
year="2018",
author="Guo, Xinlei and Wang, Tao and Fu, Hui and Guo, Yongxin and Li, Jiazhen",
volume="32",
number="3",
pages="e168-e168",
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.<p /> <p>Language: en</p>",
language="en",
issn="0887-381X",
doi="10.1061/(ASCE)CR.1943-5495.0000168",
url="http://dx.doi.org/10.1061/(ASCE)CR.1943-5495.0000168"
}