Short-term Prediction of Ionospheric TEC Based on SSA-BP and LSTM

Aiming at the problem that the prediction accuracy is greatly reduced due to the irregular disturbance of ionospheric TEC during magnetic storm, a Q-Learning algorithm based on reinforcement learning is proposed to optimize the Sparrow Search Algorithm Optimized BP neural network model and Long Short-Term Memory Network model, and then a combined deep learning ionospheric TEC prediction model is established. The combined model and two single models are used to forecast the TEC data in China provided by CODE for three days. The results show that under different levels of magnetic storms (strong, medium, weak, and none), the average relative accuracies of the combined model forecast for three days are 95.9%, 95.7%, 92.6%, and 95.3%, respectively, which is about 6% higher than these of the two single models. Among them, the proportion of forecast residuals less than 1 TECu reaches 60%, 59%, 76% and 98%, which is an average increase of about 27% compared with these of the two single models.