TY  - JOUR
PY  - 2018//
TI  - Ice-jam forecasting during river breakup based on neural network theory
JO  - Journal of cold regions engineering
A1  - Guo, Xinlei
A1  - Wang, Tao
A1  - Fu, Hui
A1  - Guo, Yongxin
A1  - Li, Jiazhen
SP  - e168
EP  - e168
VL  - 32
IS  - 3
N2  - 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>
LA  - en
SN  - 0887-381X
UR  - http://dx.doi.org/10.1061/(ASCE)CR.1943-5495.0000168
ID  - ref1
ER  -