Global Ionospheric TEC Forecasting for Geomagnetic Storm Time Using a Deep Learning-Based Multi-Model Ensemble Method

Abstract

In recent years, deep learning has been extensively used for ionospheric total electron content (TEC) prediction, and many models can yield promising prediction results, particularly under quiet conditions. Owing to the ionosphere's intricate and dramatic changes during geomagnetic storms, the high-reliable prediction of the storm-time ionospheric TEC remains a challenging problem. In this study, we developed a new deep learning-based multi-model ensemble method (DLMEM) to forecast geomagnetic storm-time ionospheric TEC that combines the Random Forest (RF) model, the Extreme Gradient Boosting (XGBoost) algorithm, and the Gated Recurrent Unit (GRU) network with the attention mechanism. Seven features in 170 geomagnetic storm events, including the three components Bx, By and Bz of interplanetary magnetic field (IMF), the Kp and Dst indices of geomagnetic activity data, the F10.7 index of solar activity data and global TEC data, were used for modeling. The test set results showed that the DLMEM model can reduce the root mean square errors (RMSE) by an average of 43.6% in comparison to our previously presented model Ion-LSTM, especially during the recovery period of geomagnetic storms. Furthermore, compared to Ion-LSTM, the RMSE values of the low-, middle- and high-latitude single-station forecast TEC can be greatly decreased by 33%, 53% and 59%, respectively. It was shown that the new model allows for more precise short-term global ionospheric forecasting during geomagnetic storms, enabling real-time monitoring of ionospheric changes.