A Prediction model of relativistic electrons at geostationary orbit using the EMD‐LSTM network and geomagnetic indices

In this study, the Empirical Mode Decomposition algorithm (EMD) and the Long Short Term Memory neural network (LSTM) are combined into an EMD-LSTM model, to predict the variation of the >2 MeV electron fluxes 1 day ahead. Input parameters include the Pc5 power, AP, AE, Kp, >0.6 MeV, and historical electron flux values, are used for predictions. All the time resolution of parameters are daily integral values. As compared the prediction results of the EMD-LSTM model with other classical prediction models, the results show that the 1 day ahead prediction efficiency of the >2 MeV 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 previous 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 predictions are closer to actual observations.