# A Model-Assisted Combined Machine Learning Method for Ionospheric TEC Prediction
## 4 Conclusions  
Real-time and accurate TEC monitoring is essential for many aerospace applications. The combined prediction method based on the neural network can reflect the implicit relationship between TEC data and external features, which can be used to improve the accuracy of TEC prediction. In order to solve the defects of a single model and further improve the prediction accuracy, a new combined prediction model is proposed for TEC over Athens, Greece. The combined prediction model is a combination of the IRI model, SML-based model, BP-NN model and MMAdapGA parameter optimization model. We use TEC data from the Athens station in Greece as an example to validate the rationality of MMAdapGA-BP-NN, so the data of other stations are not verified. In future work, we will use this method to predict TEC data for more sites and longer time horizons to verify the  
Figure 13: The absolute error of predicted TEC and geomagnetic storm days bar graph: **(a)** The absolute error of predicted TEC and geomagnetic storm days in high solar activity years (2015); **(b)** The absolute error of predicted TEC and geomagnetic storm days in low solar activity years (2020).  
\begin{table}
\begin{tabular}{c c} \hline \hline \(A_{p}\) & **Geomagnetic Activity** \\ \hline \(>\)100 & Severe Storm \\
50–99 & Major Storm \\
30–49 & Minor Storm \\
16–29 & Active \\
8–15 & Unsettled \\
0–7 & Quiet \\ \hline \hline \end{tabular}
\end{table}
Table 3: The qualitative expression of geomagnetic storm intensity.  
completeness of MMAdapGA-BP-NN. By comparing with a single model, it can be found that MMAdapGA-BP-NN has higher accuracy and better prediction effect. Compared with a single neural network model, MMAdapGA-BP-NN has a better prediction effect and is more stable. Compared with the neural network optimized by a single mutation genetic algorithm, MMAdapGA-BP-NN has a higher prediction accuracy, fewer iterations, and a more stable prediction effect. This paper provides a new way of thinking about TEC prediction, and future studies are expected to extend the applicability of the proposed model to a wider range of regions and even the globe. In addition, this modeling method is expected to be used to predict other ionospheric parameters, such as peak height and critical frequency of the F2 layer. Based on the current results, the errors can be further reduced by further adjusting the parameters of the neural network, improving the neural network optimization method, adding more parameters affecting TEC into the model and other methods. In future research, we will consider the influence of more factors on TEC prediction, such as the geomagnetic storm intensity index and more complex solar activity, to further improve the accuracy and robustness of the model. It is hoped that the MMAdapGA-BP-NN model can provide a basis for the establishment of the international model.  
Conceptualization, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); methodology, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); software, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); validation, [PERSON] and [PERSON]. ([PERSON]); formal analysis, [PERSON] ([PERSON]), [PERSON] and [PERSON]. ([PERSON]); investigation, [PERSON] and [PERSON]. ([PERSON]); resources, [PERSON] ([PERSON]); data curation, [PERSON] and [PERSON]. ([PERSON]); writing--original draft preparation, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); writing--review and editing, [PERSON] and [PERSON]. ([PERSON]); visualization, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); supervision, [PERSON] and [PERSON]. ([PERSON]); project administration, [PERSON] ([PERSON]); funding acquisition, [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript. This research was funded by the State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System (No. CEMEE2022G0201).  
Not applicable.  
The authors declare no conflict of interest.