A Seq2Seq-LSTM-Attention Model for Ionospheric foF2 Prediction in the Middle Latitude Region

Predicting the critical ionospheric frequency of F2 layer (foF2) could provide guidance for satellite navigation and high frequency communications frequency selection. Models based on deep learning have been proven to forecast ionospheric variations effectively. In this letter, we propose a Seq2Seq model with long short-term memory and attention mechanism (Seq2Seq-LSTM-Attention), aiming at predicting the foF2 parameter more accurately. The training and testing of the foF2 measurements from Wuhan, China (30.6 degrees N, 114.3 degrees E) show that the proposed Seq2Seq-LSTM-Attention model can effectively capture the correlation in the foF2 sequences and outperforms several existing cutting-edge deep learning based models in prediction accuracy. The results confirm that the proposed model is able to mine the potential relationships in the foF2 sequences more deeply.