Study on Forecasting Break-Up Date of River Ice in Heilongjiang Province Based on LSTM and CEEMDAN

Abstract

In spring, rivers at middle and high latitudes in the Northern Hemisphere are prone to ice jams, which threaten the safety of hydraulic structures in rivers. Heilongjiang Province is located on the highest latitude in China, starting at 43°26′ N and reaching 53°33′ N. Rivers in Heilongjiang Province freeze in winter and break up in spring. Forecasting the break-up date of river ice accurately can provide an important reference for the command, dispatch, and decision-making of ice flood preventing and shipping. Based on the observed break-up date series of river ice from seven representative hydrological stations in Heilongjiang Province, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) was used to decompose the observed break-up date series of river ice into several subsequences, and the long-short term memory neural network (LSTM) was used to forecast the subsequences decomposed by CEEDMAN. Then, the forecast results of each subsequence were summed to obtain the forecasting value for the break-up date of river ice proceeded by CEEMDAN-LSTM. Compared with the LSTM, the forecast accuracy of CEEMDAN-LSTM for the break-up date of river ice had been significantly improved, with the mean absolute error reduced from 0.80–6.40 to 0.75–3.40, the qualification rate increased from 60–100% to 80–100%, the root-mean-square difference reduced from 1.37–5.97 to 0.95–1.69, the correlation coefficient increased from 0.51–0.97 to 0.97–0.98, and the Taylor skill score increased from 0.87–0.99 to 0.99. CEEMDAN-LSTM performed well in forecasting the break-up date of river ice in the Heilongjiang Province, which can provide important information for command, dispatch, and decision-making of ice flood control.