In this study, We construct the EMD-LSTM model, combined the Empirical Mode Decomposition algorithm (EMD) and the Long Short Term Memory neural network (LSTM), to predict the variation of the >2MeV electron fluxes. The Pc5 power and related geomagnetic indexes as input parameters are used to predict the >2MeV electron fluxes. Compared the prediction results of the model with other classical prediction models, the results shows that the one-day ahead prediction efficiency of the > 2MeV electron fluxes is above 0.80, and the highest prediction efficiency can reach 0.92 in 2011-2013, which is much better than the prediction result of classical prediction models. Selected two high-energy electron flux storm events to verify, the results indicates that the performance of the EMD-LSTM model in the period of the high-energy electron flux storm is also relatively good, especially for the prediction of high-energy electron fluxes at extreme points, and the prediction is closer to actual observation.

In this study, the Empirical Mode Decomposition algorithm (EMD) and the Long Short Term Memory neural network (LSTM) were combined into an EMD-LSTM model, to predict the variation of the >2MeV electron fluxes. Input parameters include the Pc5 power and related geomagnetic indices are used for predictions. As compared the prediction results of the EMD-LSTM model with other classical prediction models, the results show that the one-day ahead prediction efficiency of the > 2MeV electron fluxes possesses a prediction efficiency of 0.80, and the highest prediction efficiency can reach 0.93. These results are superior to the prediction accuracy of more traditional models. Using two high-energy electron flux storm events for validation, the results indicate that the performance of the EMD-LSTM model in the period of the high-energy electron flux storm is also relatively good, especially for the prediction of high-energy electron fluxes at extreme points, and the prediction is closer to actual observation.