# Forecasting Ionospheric foF2 Based on Deep Learning Method
## 3 Results and Discussion
### Diurnal Variations of Forecasting Models  
To compare the forecasting ability of the diurnal variations of foF2, three-day observed data of foF2 in the high solar activity year (2015) and the low solar activity year (2019) are selected to compare with the forecasting results. The measurements are plotted together with the results from BPNN, GABP, IRI2016, and LSTM models during days 71-74 and 256-259 in 2019 under geomagnetic quiet conditions at Brisbane, Hobart, Canberra, and Darwin, as shown in Figure 6.  
Figure 6 shows that all 4 models can capture the general trends of foF2 diurnal variations. In most cases, the result of the BPNN model, the GABP model, and the deep learning LSTM model are better than that of the IRI2016 model. Among them, the LSTM model tends to be closer to the measured curve than other forecasting models, especially  
Figure 6: Samples of comparisons of daily variations for observed (blue solid line) and 4 models forecasting foF2 values during a quiet period in the year of solar minimum (2019) during days 71–74 and 256–259. at the peaks, some subtle changes can be reflected. For instance, there is a sudden drop of observed peak value at station BRI near day 72, only the LSTM model successfully reproduces this drop, while the BPNN model and the IRI2016 model still rise in peak. To investigate the forecasting ability of foF2 diurnal variability during geomagnetic storm conditions, two storm events that occurred in 2015 are selected for further analysis. Figure 7 shows the variation of the solar geomagnetic activity index Dst, Kp, and AE during two geomagnetic storm periods, where the Dst, Kp, AE data are provided by the website at [[https://omniweb.gsfc.nasa.gov/form/dx1.html](https://omniweb.gsfc.nasa.gov/form/dx1.html)]([https://omniweb.gsfc.nasa.gov/form/dx1.html](https://omniweb.gsfc.nasa.gov/form/dx1.html)), accessed 12 May 2021. The storm event occurred during days 76-79 and 173-176 of 2015. The minimum Dst index of the two storm events is below \(-200\) nT, reaching the level of severe geomagnetic storms [36]. Figure 8 is the same format as Figure 6 but during geomagnetic storm conditions. The comparison between the measurements and the forecasting results for three days successively at SAY, SHY, WUH, and BEJ. The geomagnetic storm at day 76 has a significant impact on the variations of foF2, and sudden drops of foF2 peak values near day 77 are observed at all stations, but only the LSTM model captured this variation trend, while the BPNN model, the GABP model, and IRI2016 model show a relatively smooth variation in peak as that in quiet condition, accordingly producing forecasting errors to some extent. In contrast, the variation of the peak value of observed foF2 is not obvious during the magnetic storm of day 173-176 in 2015, and all the forecasting models are able to capture the general trend of observed diurnal variations. Figure 9 shows the absolute deviation dfoF2 of Figure 8 to evaluate the forecasting errors of the models specifically, which is calculated by the subtraction of forecasting and observed values. As can be seen from the left panels of Figure 9, the absolute deviation of the LSTM model shown in the purple stem is relatively smaller than other models, particularly around peak values at day 77, indicating that the LSTM model successfully forecasts the general diurnal variation of foF2 behavior in geomagnetic storm periods. The right panels of Figure 9 show that there are some data gaps in the geomagnetic storm period of day 173-175, but the overall trend of absolute deviation shows that the LSTM model is also relatively small among all models. Figure 7: Variation of Dst, Kp, and AE index during the geomagnetic storm period of (**a**) days 73–83 in 2015; (**b**) days 169–179 in 2015. The variation of the magnetic Dst index during this storm is shown on the top panel, the red dash lines in horizontal indicate the Dst at the level of \(-200\) nT, and the vertical red dash lines indicate the storm periods.