Short-term regional ionospheric TEC forecast using a hybrid deep learning neural network

Abstract

The total electron content (TEC) of the ionosphere is one of the key parameters for both scientific study and engineering application. A hybrid deep learning network based on Grapy Convolution Network (GCN) and Long Short Term Memory (LSTM) is proposed to predict the regional ionospheric TEC 24 h in advance across China. The extensive 14-year dataset during 2005–2018 from the CODE TEC measurements, covering a wide variety of solar activity, are used to comprehensively evaluate the performance of the predictive model. The experimental results show that the model predicted TEC is in good agreement with the actual CODE TEC. Over 90% of differences between the actual TEC and the predicted TEC are within ±2 TECU, and the model errors obviously depend on seasons, latitudes and solar activity. Comparison with the previous work indicate that the proposed regional forecast model is competitive.