## 5 Discussion
### Test Results  
The test results of the IRI-CCIR, IRI-URSI, SML model, and SVR model for the period from April 2006 to March 2007 are shown in Figure 8: (a) and (b) correspond to Table 2, which shows the specific configuration when the predictions of CCIR and URSI are obtained. \begin{table}
\begin{tabular}{c c c} \hline
**Options** & **Implication** & **Selection** \\ \hline Ne Topside & The model of electron density in the & NeQuick \\  & topside ionosphere & \\ FoF2 Model & The model of FoF2 & CCIR or URSI-88 \\ FoF2 Storm & The model for calculating FoF2 during & OFF \\  & a storm & \\ hnr2 Model & The model of hnr2 & M3000F2 \\ Bottomside Thickness B0 & The model of the F2 bottom side region & Bil-2000 \\ F1 Model & The model of F1 layer & Scotto-1997-no-L \\ D & The model of D layer & IRI-1990 \\ Te & The model of electron temperature & TBT-2012 \\ Ion Comp Model & The model of densities and composition & DS95/DY85 \\ \hline \end{tabular}
\end{table} 
Table 2: IRI model optional parameter configuration. Figure 7: The approach to modeling TEC using SML. Figure 9 compares the OBSs and prediction results of the four models from April 2006 to March 2007, revealing the following:  
1. All four models can fit the diurnal variation trend of TEC, but their fitting capabilities vary;
2. The CCIR and the URSI models overestimate TEC values around UT = 0;
3. December 2006 and January 2007, the SML model predicted negative values around UT = 5, which is counterintuitive. Figure 8: Obtaining test values for CCIR, URSI, SML, and SVR models: (**a**) method for obtaining CCIR prediction results; (**b**) method for obtaining URSI prediction results; (**c**) method for obtaining URSI prediction results; (**d**) method for obtaining SVR prediction results. To comprehensively assess the prediction ability of all models, in addition to calculating the RMSE of the models, we considered the mean absolute error (MAE) between the predictions and the OBSs. The calculation formula is as follows:  
\[\text{MAE}= \frac{1}{N}\sum_{i=1}^{N}\bigl{(}\text{TEC}^{\prime}_{i}-\text{ TEC}_{i}\bigr{)}, \tag{9}\]  
where TEC\({}^{\prime}_{i}\) represents the model prediction value, TEC\({}_{i}\) represents the model observed value, and \(N\) represents the total amount of data. Figure 10 gives the MAE and RMSE of the four models. The results indicate the following:  
1. Within the twelve months, the CCIR model had the most significant prediction error for four months, the URSI model for seven months, and the SML model for one month. 2. From the perspective of MAE, in May 2006, the SVR model had a larger MAE than the URSI and SML models, and in January 2007 and February 2007, the SVR model had a larger MAE than the SML model. Except for these months, the SVR model had the most petite MAE for the remaining months. 3. From the perspective of RMSE, in May 2006, the SVR model had a larger RMSE than the URSI and SML models, and in January 2007 and February 2007, the SVR model had a larger RMSE than the SML model. Except for these months, the SVR model had the smallest RMSE for the remaining months. Figure 11 presents the statistics of MAE and RMSE between the predictions and OBSs by model. From the graph: (1) The SVR model has the most petite MAE, at 0.54 TECU, reduced by 0.52 TECU compared to the CCIR model, 0.87 TECU compared to the URSI model, and 0.16 TECU compared to the SML model. (2) The SVR model has the smallest RMSE, at 0.68 TECU, reduced by 0.68 TECU compared to the CCIR model, 0.94 TECU compared to the URSI model, and 0.24 TECU compared to the SML model. Figure 9: Comparison of CCIR, URSI, SML, and SVR model test values with observations (OBS): (**a**) April 2006; (**b**) May 2006; (**c**) June 2006; (**d**) July 2006; (**e**) August 2006; (**f**) September 2006; (**g**) October 2006; (**h**) November 2006; (**i**) December 2006; (**j**) January 2007; (**k**) February 2007; (**l**) March 2007. The results indicate that the TEC predicted using the SVR method is closer to the observations than those predicted by the IRI and SML models.