Forecasting Ionospheric foF2 Using Bidirectional LSTM and Attention Mechanism.

The critical frequency of ionospheric F2 layer (foF2) is an important ionospheric characteristic parameter. In this paper, a deep learning model based on Bidirectional long short‐term memory (BiLSTM) and attention mechanism is implemented for predicting the foF2 parameter. The inputs of models are the foF2 of globally available ionospheric ionosonde stations, geographic longitude and latitude, world time (UT), geomagnetic activity index, and solar activity index from 2015 to 2017. The superiority of the model is analyzed from different latitudes, seasons, and geomagnetic conditions. The results show that the prediction performance of the Bidirectional long short‐term memory model based on attention mechanism (BiLSTM‐Attention) is better than other models. The performance of the prediction model is optimal at high latitudes. The root mean square error (RMSE) and correlation coefficient (R) of the BiLSTM‐Attention model are 0.539 MHZ and 0.908 MHz at high latitudes, respectively. In terms of RMSE, it is 25.243%, 18.209%, and 11.203% lower than those of the international reference ionosphere (IRI), LSTM, and BiLSTM models, respectively. The prediction results of the four seasons show that the models are more applicable in winter. Compared with the IRI model, the RMSE of the BiLSTM‐Attention model in spring, summer, autumn, and winter is reduced by 24.344%, 21.181%, 25.058%, and 30.948%, respectively. The prediction effect of the BiLSTM‐Attention model is improved in the magnetic quiet period, the magnetic moderate period and the magnetic storm period. Also, the improvement effect is more obvious in the magnetostatic day, and the RMSE is reduced by 27.462% compared with the IRI model. Plain Language Summary: The critical frequency of ionospheric F2 layer (foF2) is an important parameter to characterize the variability of the ionosphere. In this study, we present an effective method to predict the foF2 based on ionosonde stations distributed around the world. The inputs to these models are the foF2 values, geographic longitude and latitude, world time (UT), geomagnetic activity index, and solar activity index from 2015 to 2017. Compared with the international reference ionosphere (IRI‐2016) model, long short‐term memory (LSTM) model, and Bidirectional long short‐term memory (BiLSTM) model, the Bidirectional long short‐term memory model based on attention mechanism (BiLSTM‐Attention) can achieve better performance under different latitudes, seasons and geomagnetic conditions. Key Points: The Bidirectional long short‐term memory (BiLSTM)‐Attention model is first used to predict the foF2 based on ionosonde stations distributed around the worldGeomagnetic activity index and solar activity index are added for the foF2 prediction of these modelsThe BiLSTM‐Attention prediction model shows better performance than the IRI‐2016, LSTM, and BiLSTM prediction models under different latitudes, seasons and geomagnetic conditions [ABSTRACT FROM AUTHOR]