# Forecasting Ionospheric foF2 Based on Deep Learning Method
## 2 Models and Data
### Data and Processing
#### 2.3.1 Data Sets
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Year** & **2006** & **2007** & **2008** & **2009** & **2010** & **2011** & **2012** \\ \hline BEJ & 3973/8760 & 8637/8760 & 8327/874 & 7215/8760 & 6567/8760 & 6935/8760 & 8012/8784 \\  & (45.35\%) & (98.60\%) & (94.80\%) & (82.36\%) & (74.97\%) & (79.17\%) & (91.21\%) \\ MOH & / & / & / & / & 2643/8760 & 8110/8760 & 8012/8784 \\  & & & & & (30.17\%) & (92.58\%) & (91.21\%) \\ WUH & / & / & / & / & 2845/8760 & 583/8760 & 8523/87860 & 8523/8784 \\  & & & & (32.48\%) & (67.04\%) & (97.03\%) \\ SAY & / & 1507/8760 & 7374/8784 & 6587/8760 & 7958/8760 & 8194/8760 & 6261/8784 \\  & & & (17.20\%) & (83.95\%) & (75.19\%) & (90.84\%) & (93.54\%) & (71.28\%) \\ SHY & / & / & / & / & / & / & 5641/8784 \\  & & & & & & (64.22\%) \\ \hline
**Year** & **2013** & **2014** & **2015** & **2016** & **2017** & **2018** & **2019** \\ \hline BEJ & 8626/8760 & 8521/8760 & 8672/8760 & 8733/8784 & 7735/8760 & 8423/8760 & 6538/8760 \\  & (98.47\%) & (97.27\%) & (99.00\%) & (99.42\%) & (88.30\%) & (96.15\%) & (74.63\%) \\ MOH & 8332/8760 & 8628/8760 & 8132/8760 & 8649/8784 & 8635/8760 & 8585/8760 & 8422/8760 \\  & (95.11\%) & (98.49\%) & (92.83\%) & (98.46\%) & (98.57\%) & (98.00\%) & (96.14\%) \\ WUH & 8372/8760 & 8666/8760 & 7323/8760 & 8617/8784 & 8542/8760 & 8665/8760 & 8542/8760 \\  & (95.57\%) & (98.93\%) & (83.60\%) & (98.10\%) & (97.51\%) & (98.92\%) & (97.51\%) \\ SAY & 8273/8760 & 8461/8760 & 8593/8760 & 8590/8784 & 8595/8760 & 7168/8760 & 8669/8760 \\  & (94.44\%) & (96.59\%) & (98.09\%) & (97.79\%) & (98.12\%) & (91.83\%) & (98.96\%) \\ SHY & 8420/8760 & 8542/8760 & 8556/8760 & 8446/8784 & 8689/8760 & 7991/8760 \\  & (96.12\%) & (97.51\%) & (97.67\%) & (96.15\%) & (99.19\%) & (91.22\%) & / \\ DAR & / & 8574/8760 & 6959/8760 & 8367/8784 & 8492/8760 & 7520/8760 & 8439/8760 \\  & / & (97.88\%) & (79.44\%) & (95.25\%) & (96.94\%) & (85.84\%) & (96.34\%) \\ BRI & / & 8739/8760 & 7982/8760 & 8643/8784 & 8332/8760 & 8073/8760 & 8559/8760 \\  & & (99.76\%) & (91.12\%) & (98.39\%) & (95.11\%) & (92.16\%) & (97.71\%) \\ CAM & / & 7962/8760 & 7557/8760 & 7245/8784 & 8407/8760 & 3509/8760 & 2816/8760 \\  & / & (90.89\%) & (86.27\%) & (82.48\%) & (95.97\%) & (40.06\%) & (32.15\%) \\ CAN & / & 8620/8760 & 7752/8760 & 7908/8784 & 8509/8760 & 6552/8760 & 7593/8760 \\  & / & (98.40\%) & (88.49\%) & (90.03\%) & (97.13\%) & (74.79\%) & (86.68\%) \\ HOB & / & 8014/8760 & 6934/8760 & 7261/8784 & 8177/8760 & 7757/8760 & 7945/8760 \\  & & (91.48\%) & (79.16\%) & (82.66\%) & (93.34\%) & (88.55\%) & (90.70\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Data availability of ionosonde stations during the period of 2006–2019. The data of the IRI2016 model for 10 ionosonde stations listed in Table 2 from 2006 to 2019 is utilized for comparison with other forecasting models. IRI is an international project sponsored by the Committee on Space Research (COSPAR) and the International Union of Radio Science (URSI). The IRI model is one of the most widely recognized models, which has often been used to estimate the prediction performances for ionospheric models. The IRI model provides the specification of ionospheric parameters based on all worldwide available data from ground-based as well as satellite observations, which is continuously being improved by the IRI working group [47]. Currently, the latest version of IRI is the IRI2016 model, which is used in our work. For a given location, time, and date, the IRI2016 model provides an empirical estimation of foF2 values based on the monthly averages. A web interface for computing and plotting IRI values are accessible from the IRI homepage at [[http://irimodel.org/](http://irimodel.org/)]([http://irimodel.org/](http://irimodel.org/)), accessed on 10 April 2021.