Ice floods occasionally occur during river ice breaking up in northern China in spring,threatening the safety of hydraulic structures.Forecasting the break-up date of river ice(BUDRI)accurately is an important refer-ence for anti-flooding command and dispatching decision-making during ice breaking period.For forecasting the BUDRI in northern China,the observed break-up date series of river ice of 5 representative hydrological stations in Heilongjiang province located in northern China was selected,and the Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise technology and deep learning model Long Short Term Memory(CEEMDAN-LSTM)was used to forecast the BUDRI.The results show that the forecast accuracy of CEEMDAN-LSTM,com-pared with LSTM,had been significantly improved with the mean absolute error reduced from 2.51 d to 1.20 d,the qualification rate increased from 91.59％to 100％in the training period.and the mean absolute error reduced from 3.85 d to 1.65 d,the qualification rate increased from 88％to 96％in the validation period.The CEEMDAN-LSTM performed well in forecasting the BUDRI in northern China,which can provide important information for command,dispatch,and decision-making of ice flood control.
关键词
河流开河日期/信号分解技术/深度学习/预报方法/北方寒区
Key words
break-up date of river ice/signal decomposition technology/deep learning/forecasting method/northern China
维普
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