# Forecasting Ionospheric foF2 Based on Deep Learning Method
## 4 Conclusions  
In this paper, an LSTM model based on deep learning for foF2 forecasting is developed. To verify the forecasting ability and performance of the deep learning LSTM model, a BPNN model and a GABP model are constructed for comparison and analysis. Input parameters of these models including the factors associated with geographic locations, solar activity, solar cycle, magnetic activity, neutral wind, diurnal information, and seasonal information. All the forecasting models are compared with that of the observed data under different geomagnetic conditions. The results illustrate that all models can successfully forecast the general diurnal and seasonal shape of foF2 behavior, but the deep learning LSTM model is the closest to the observed foF2 values. By testing against the international reference  
Figure 11: Same format as Figure 10, but for the year 2015.  
ionosphere IRI2016 model, we found that the deep learning LSTM model forecasting overperforms the empirical model for both quiet and active geomagnetic conditions. Under different solar geomagnetic activities, the results of the four models are compared and analyzed. Compared with the BPNN model and the GABP model, the LSTM model which can shed light on sequential variation in time-series data shows significantly better performance in forecasting foF2 values, especially during geomagnetic storm days, only the LSTM model can capture the variations for sudden drops of foF2 peak values. We can infer that the deep learning methods are promising in the application for forecasting ionospheric parameters in contrast to the traditional neuron network methods. While the accuracy of the deep learning LSTM model is the optimal one of all these models, there are still some degrees of forecasting deviation. An improvement over the LSTM model can be achieved if data from more stations are included in the training of the deep learning network in the future. In addition, some other deep learning methods could be explored for the forecasting of foF2 and also deep learning methods in the forecasting of some other ionospheric parameters namely hmF2 (peak heights of the F2 layer), M3000F2 (M factor of F2 layer), and TEC. Moreover, further studies can be explored to improve the accuracy of the deep learning LSTM model, and significant developments to the LSTM model are needed for mapping foF2 on a global scale in the short term, or even in real-time forecasting for our future studies. Conceptualization, methodology, software, visualization, writing, X.L. ; conceptualization funding acquisition, coordination, C.Z. ; data collection, review, and editing, Q.T. ; methodology, J.Z. ; software, F.Z. ; data collection, G.X. ; review and editing, Y.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (No.41574146, 41774162, 42074187), the National Key R&D Program of China (No. 2018 YFC1503506), and the Excellent Youth Foundation of Hubei Provincial Natural Science Foundation (No. 2019 CFA054). Not applicable. Data Availability Statement: Data sharing is not applicable to this article. The authors would like to gratefully thank the anonymous reviewers for their insight and help. The hourly foF2 data of 10 selected ionosonde stations are available by contacting [PERSON] ([EMAIL_ADDRESS]). The authors would like to appreciate the usage of the National Oceanic and Atmospheric Administration (NOAA) datasets and the usage of datasets from the World Data Centre (WDC) for Geophysics, Beijing. The authors declare no conflict of interest. List of abbreviations used in the paper.