Citation

Guo X, Wang T, Fu H, Guo Y, Li J. J. Cold Reg. Eng. 2018; 32(3): e168.

Copyright

(Copyright © 2018, American Society of Civil Engineers, Technical Council on Cold Regions Engineering)

DOI

10.1061/(ASCE)CR.1943-5495.0000168

PMID

unavailable

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.

Language: en