# A Machine Learning-Based Method for Modeling TEC Regional Temporal-Spatial Map
## 2 Modeling
### Modeling Dataset  
As previously mentioned, ML is a data-driven modeling approach. In simple terms, the process of ML can be summarized as determining the learning model, strategy, and algorithm based on the determined dataset, finally finding the optimal model through training, then using the proposed model to predict the unknown data. Therefore, the selection of appropriate modeling data is an important prerequisite to ensuring the accuracy of modeling. The data used for modeling and validation in this paper are obtained from the National Oceanic and Atmospheric Administration (NOAA) and the Global Ionospheric Radio Observatory (GIRO). This paper focuses on exploring an ML-based method for modeling the TEC temporal-spatial map, and the TEC provided by NOAA and GIRO is input data to test this method's performance. These data have good credibility and were selected for modeling, as reported by [54]. The data information is listed in Table 1. We can download the required data by selecting the corresponding time, observed station, and data type, through the official websites of NOAA [55] and GIRO [56]. The downloaded data can be saved as a text file in which recorded 24 h of TEC data for each day of the year at a station; that is, each line gives the TEC at one moment. Specifically, part of the data of the Pruhonice, Roquetes, Rome, etc., stations are obtained through NOAA, part of the data of the Athens, Fairford, Dourbes, etc., stations, and all the data of the Sopron and Nicosia stations used for validation are obtained through GIRO. Figure 3 shows the geographical distribution of the \(11\) ionospheric stations used for modeling and validation in this paper in parts of Europe. The monthly mean valid data volume of TEC is 42,049 in total. To ensure the data continuity and the model's validity, we selected that the continuous period of each station's valid data is longer than one solar activity cycle, which covers at least one low solar activity year and one high solar activity year (as shown in Figure 4). As mentioned above, the observation data of different periods at the same station are obtained from NOAA and GIRO, respectively. Considering that the TEC data provided by GIRO has a longer continuous period and a larger amount, the missing periods in GIRO were supplemented by NOAA and then merged into a group for modeling and validation. For some stations whose valid data continuous period is less than one solar activity cycle, such as the Sopron, Nicosia, and Chilton, we take them as validation stations and not as modeling stations to participate in the modeling process. For the collected data, we first preprocessed them in the following two steps: \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**No.** & **Station** & **Latitude** (\({}^{\circ}\)N) & **Longitude** (\({}^{\circ}\)E) & **Duration of** **Valid Data** & **Data** & **Data Sources** & **Receiver** & **Including in** **Modeling** \\ \hline
1 & Athens & 38.00 & 23.50 & 2004–2021 & 4948 & \(\surd\) & \(\surd\) & DPS-4 & Yes \\ \hline
2 & Chilton & 51.60 & \(-1.30\) & 2005–2009 & 1417 & \(\surd\) & \(\surd\) & DPS-1 & No \\ \hline
3 & Dourbes & 50.10 & 4.60 & 2004–2021 & 5160 & \(\surd\) & \(\surd\) & DGS-256 & Yes \\ \hline
4 & El Arensillo & 37.10 & \(-6.70\) & 2004–2017 & 3834 & \(\surd\) & \(\surd\) & DGS-256 & Yes \\ \hline
5 & Fairford & 51.70 & \(-1.50\) & 2004–2021 & 4726 & \(\surd\) & \(\surd\) & DGS-256 & Yes \\ \hline
6 & Nicosia & 35.10 & 33.20 & 2013–2016 & 1546 & - & \(\surd\) & DPS-4D & No \\ \hline
7 & Pruhonice & 50.00 & 14.60 & 2004–2021 & 5136 & \(\surd\) & \(\surd\) & DPS-4 & Yes \\ \hline
8 & Rome & 41.80 & 12.50 & 2006–2021 & 4584 & \(\surd\) & \(\surd\) & DPS-4 & Yes \\ \hline
9 & Roquetes & 41.00 & 0.00 & 2004–2021 & 5160 & \(\surd\) & \(\surd\) & DGS-256 & Yes \\ \hline
10 & San Vito & 40.60 & 17.80 & 2004–2021 & 4779 & \(\surd\) & \(\surd\) & DGS-256 & Yes \\ \hline
11 & Sopron & 47.63 & 16.72 & 2019–2021 & 759 & - & \(\surd\) & DPS-4D & No \\ \hline \hline \end{tabular}
\end{table}