Ionospheric TEC Prediction Based on QPSO-LSTM Model

For the ionospheric TEC short-term prediction of a single LSTM model, there are difficulties in parameter adjustment and performance Optimization, resulting in low prediction accuracy. Quantum Particle Swarm Optimization (QPSO) and LSTM model are combined. The quantum particle swarm optimization algorithm was used to determine the optimal solution, optimize the parameter configuration of the LSTM model, and use the model to predict the ionospheric TEC of low, middle and high latitudes 5 d in advance for three periods in 2014 and 2018, and analyze the prediction accuracy of the ionospheric TEC during the quiet period and disturbance period of geomagnetic activity. The experimental results show that when the LSTM model optimized by QPSO is used to predict TEC for 5 consecutive days, compared with the single LSTM model, the root-mean-square error of the QPSO-LSTM model is reduced by 0.34 TECU at most in low solar activity years, and the relative accuracy is increased by 2.68% at most in high solar activity years. The RMS error decreases by up to 0.68 TECU at low latitudes, while the relative accuracy increases by up to 2.36% at high latitudes. From different analysis angles, it is found that the prediction accuracy of QPSO-LSTM model is better than that of single LSTM model.