Spatio-Temporal Ionospheric TEC Prediction Using a Deep CNN-GRU Model on GNSS Measurements

Ionospheric variability and disturbances can affect technologies in space and on Earth, disrupting satellite operations, communications networks, and navigation systems. The availability of numerous satellites deployed by GPS, GLONASS, Galileo, BeiDou navigation systems allows continuous monitoring of the Earth's ionosphere using measurements from these satellites. Here, we scrutinize the effectiveness and efficiency of a convolutional enriched recurrent neural network for spatio-temporal VTEC prediction. In our analysis, we have chosen different years under different solar and geomagnetic activity. We test our models for different days and at various latitudes to see model's response in cases of high ionosphere activity. Our experiments indicate that the proposed combined deep CNN-GRU model is capable of providing an accurate prediction of TEC values even in intense conditions.