## 5 Results and Discussion
### Quantitative Analysis of the ConvGRU Models Efficacy
The superior performance of the ConvGRU-L\({}_{\text{C}}\) method across both peaks and troughs of solar activity underscores its potential as a reliable tool for ionospheric forecasting. The efficacy of three distinct ConvGRU models--ConvGRU-L\({}_{\text{C}}\), ConvGRU-L\({}_{\text{S}}\), and ConvGRU-L\({}_{\text{log-cosh}}\)--was critically analyzed to assess their predictive accuracies in modeling ionospheric scintillations over the South American region during January, March, and July for the years 2015 and 2020. This analysis specifically focused on the test data set covering the last three days of each month (29 th to 31 st), which accounted for 10% of the total dataset, providing a concise snapshot of model performance during varied ionospheric conditions. The ConvGRU-L\({}_{\text{C}}\) model demonstrated substantial consistency and robustness throughout the testing periods, particularly during times of heightened ionospheric activity. In January and March of 2015, the model attained a CC of 0.80, indicating a strong linear relationship, despite the increased disturbances within the ionosphere. A slight improvement was observed in July of the same year, with a CC of 0.81, suggesting marginally better alignment with observed data, which may be influenced by seasonal variations in ionospheric conditions. Despite these strong performances, the RMSE and ME for March 2015 were relatively high at 0.024 and 0.04, respectively. This increase could be attributed to the complex ionospheric dynamics typically exacerbated during the equinox, impacting geomagnetic activity. Conversely, the year 2020 showcased significant advancements in model accuracy, achieving a peak CC of 0.83 in January, alongside the lowest recorded RMSE (0.013) and ME (0.02). These metrics indicate an enhanced predictability during periods of minimal solar activity, which offers more stable conditions favorable to model performance. The ConvGRU-L\({}_{\text{S}}\) model exhibited lower performance metrics compared to the ConvGRU-L\({}_{\text{C}}\) model, particularly under conditions of high solar activity. In July 2015, this model recorded a CC of 0.78 and the highest RMSE of 0.03 among the models, highlighting difficulties in adapting to rapid changes in ionospheric parameters. However, by 2020, there were notable improvements in the model's performance, with January showing a CC increase to 0.81 and an RMSE reduction to 0.022, reflecting its better handling of quieter geomagnetic conditions. Maintaining consistent performance throughout the observed periods, the ConvGRU-L\({}_{\text{log-cosh}}\) method showed resilience across 2015, with CC values consistently around 0.75 for all months, suggesting a robust model design capable of managing external disturbances efficiently. This consistency extended into 2020, with notable improvements in January where the CC increased to 0.8 and the RMSE and ME decreased to 0.02 and 0.04, respectively. These improvements underscore the method's enhanced capability in managing fewer disturbances, aligning with the general trend of improved performance under quieter geomagnetic conditions. Throughout the analysis, it was evident that the ConvGRU-L\({}_{\text{C}}\) model outperformed the other techniques, particularly in conditions of high solar activity, by maintaining higher CC values and lower error metrics. In contrast, the ConvGRU-L\({}_{\text{S}}\) method, despite showing potential, requires further optimization to enhance its robustness against high-disturbance conditions. The ConvGRU-L\({}_{\text{log-cosh}}\) method, occupying a middle ground, offered consistent performance that, while slightly less optimal than that of the ConvGRU-L\({}_{\text{C}}\), proved dependable across varied conditions. The higher occurrences of ionospheric scintillation in 2015 correlated with increased RMSE and ME values across all models, particularly noticeable in March.