## 5 Results and Discussion
### Quantitative Analysis of the ConvGRU Models Efficacy
This period, accounting for 90% of the total data available, included a training set comprising 70% of the total dataset (1 st to 22 nd) and a validation set forming 20% (23 rd to 28 th). Data from the subsequent testing phase (the last three days of each month) were excluded from the modeling process to ensure the integrity and independence of the evaluation. The ConvGRU-L\({}_{\text{C}}\) method exhibited commendable consistency throughout periods of heightened ionospheric activity. During January and March of 2015, the method achieved CC of 0.91, demonstrating a strong linear relationship despite increased disturbances in the ionosphere. In July 2015, a slight improvement was noted with a CC of 0.92, suggesting marginally better alignment with observed data, potentially influenced by seasonal variations in ionospheric conditions. However, the RMSE and ME for March were slightly higher (RMSE = 0.009, ME = 0.017), which may be attributed to the complex ionospheric dynamics that are typically exacerbated during the equinox, affecting geomagnetic activity. In contrast, the year 2020 showed significant improvements in model accuracy, achieving a peak CC of 0.94 in January, along with the lowest recorded RMSE (0.006) and ME (0.011). These metrics indicate enhanced predictability during periods of minimal solar activity, which offer more stable conditions favorable to model performance. The ConvGRU-L\({}_{\text{log-cosh}}\) method maintained consistent performance throughout 2015, with a CC of 0.89 for all months studied. This consistency suggests a robust model design, albeit with a margin of error possibly influenced by external disturbances not fully captured by the model's architecture. The RMSE and ME values remained relatively steady around 0.013 and 0.023, respectively, indicating a moderate level of prediction error that showed little fluctuation over the months. Notable improvement was observed in 2020, with a CC increase to 0.91 in January. The decrease in RMSE and ME during this year reflects the method's enhanced capability in managing fewer disturbances, aligning with the general trend of improved performance under quieter geomagnetic conditions. Conversely, the ConvGRU-L\({}_{\text{8}}\) method displayed the lowest performance metrics among the three models. In January 2015, this method recorded the highest RMSE of 0.026, struggling significantly under conditions of high solar activity. The elevated error rates highlight difficulties in the model's ability to adapt to rapid changes in ionospheric parameters, which are more pronounced during periods of intense solar activity. Although there were improvements in 2020, they were not as marked as those observed with the other methods, suggesting that, while the ConvGRU-L\({}_{\text{8}}\) method may perform better under quieter conditions, it still lacks the robustness needed to match the accuracy levels of ConvGRU-L\({}_{\text{C}}\) and ConvGRU-L\({}_{\text{log-cosh}}\). The overall analysis indicates that the ConvGRU-L\({}_{\text{C}}\) method outperforms the other techniques, particularly in high solar activity conditions, by maintaining higher CC values and lower error metrics. In contrast, the ConvGRU-L\({}_{\text{S}}\) method, despite showing some potential, requires further optimization to improve its robustness against high-disturbance conditions. The ConvGRU-L\({}_{\text{log-cosh}}\) method occupies a middle ground, offering consistent performance that, while slightly less optimal than that of the ConvGRU-L\({}_{\text{C}}\) method, remains dependable. The higher occurrences of ionospheric scintillation in 2015 correlate with increased RMSE and ME values across all models, particularly in March. This trend underscores the challenges of modeling under intense ionospheric fluctuations, and highlights the superior adaptability of the ConvGRU-L\({}_{\text{C}}\) method during such periods. The extended analysis emphasizes that the choice of modeling technique significantly impacts the accuracy of ionospheric predictions, especially under varying solar activities.