## 7 Conclusions  
This study investigated the relationship between geomagnetic activities and ionospheric scintillations, particularly focusing on how solar and geomagnetic parameters influence ionospheric conditions across various temporal frameworks and latitudinal extents. By integrating critical indices such as Kp, Dst, sunspot numbers, and the F10.7 solar flux, we elucidated the complex dynamics governing ionospheric disturbances. These disturbances hold significant ramifications for the dependability of satellite communications and navigation systems, especially prevalent in regions of low latitude. Our findings accentuated the pronounced impact that fluctuations in geomagnetic conditions exert on the prevalence of ionospheric scintillations. High solar activity periods, marked by elevated sunspot and F10.7 index values, coincide with intensified geomagnetic disturbances as indicated by escalated Kp and Dst indices. This escalation is accompanied by increased ionospheric scintillation activities, particularly occurring during the solar maximum of 2015. In contrast, the relatively calm geomagnetic conditions observed in 2020--a period of solar minimum--were associated with reduced scintillation activities, thus highlighting a direct correlation between solar activity levels and ionospheric disturbances. Additionally,this study emphasized the temporal variability of geomagnetic effects on ionospheric scintillation by examining specific months that represent different seasonal conditions--the winter and summer solstices, and the equinoxes. This seasonal analysis exposed distinct patterns that aligned with anticipated increases or decreases in ionospheric scintillation occurrences based on geomagnetic indices and solar activity fluctuations. Innovatively, this research utilized advanced machine-learning techniques, particularly the application of ConvGRU networks, to model and predict ionospheric scintillation. This approach leveraged the power of artificial intelligence and deep learning to manage the complexities of spatiotemporal data intrinsic to ionospheric phenomena. Employing neural network architectures, including RNNs, GRUs, and ConvGRUs enhanced our capability to predict dynamic changes in the ionosphere with greater accuracy. The ConvGRU model, a centerpiece of this analysis, marks a significant advancement by integrating convolution operations within the GRU framework. This integration enables the ConvGRU model to effectively capture both temporal sequences and spatial distributions, particularly accommodating ionospheric two-dimensional data. The model's ability to handle the intrinsic variability of ionospheric data and improve prediction accuracy by addressing long-term dependencies and spatial feature recognition is a notable enhancement over traditional neural network methods. In this research, we utilized ground-based GNSS data from 121 stations across South America to model and predict ionospheric scintillations under varying solar and geomagnetic conditions during January, March, and July of 2015 and 2020. By comparing ionospheric behaviors during these periods, our study not only illuminated the dynamic nature of ionospheric disturbances but also refined methodologies to predict such phenomena with enhanced precision. This was particularly evident in how higher TEC values correlated with increased scintillation occurrences near equatorial ionospheric anomalies--regions notoriously susceptible to heightened geomagnetic disturbances. Key findings from the study underscore the impact of the solar cycle on ionospheric conditions, with 2015 experiencing more frequent and intense scintillation events compared to 2020. The ConvGRU models (ConvGRU-L\({}_{\text{C}}\), ConvGRU-L\({}_{\text{log-cosh}}\), and ConvGRU-L\({}_{\text{S}}\)), tailored specifically for this study, demonstrated varying degrees of effectiveness, with the ConvGRU-L\({}_{\text{C}}\) model generally outperforming others in terms of accuracy and reliability across different testing phases.