## 5 Results and Discussion  
This study focused on the modeling and prediction of ionospheric scintillation, utilizing observational data with standardized time steps as input and target data for a ConvGRU. The study employed a total of 121 ground-based GNSS stations, spanning a latitude range of 15\({}^{\circ}\)N to 55\({}^{\circ}\)S and a longitude range of 270\({}^{\circ}\) to 330\({}^{\circ}\)E across South America (local time approximately Universal Time--05:00), positioned within or in proximity to the EIA. The temporal dynamics of ionospheric phenomena over the course of a year can be stratified into three distinct seasonal phases: the summer solstice, the winter solstice, and the equinoxes. In this study, a systematic approach was adopted to select one representative month from each season to conduct modeling and prediction analyses of ionospheric phenomena. Additionally, this study acknowledged the influence of the eleven-year solar cycle on ionospheric conditions. Notably, the year 2015 was characterized by heightened solar activity, while 2020 experienced comparatively lower solar activity. The primary objective of this article was to predict ionospheric scintillation under varying geomagnetic conditions. For this purpose, January, March, and June were strategically chosen for 2015 and 2020 to capture contrasting solar activity phases and their respective impacts on ionospheric conditions. The year 2015, marked by peak solar activity, and 2020, characterized by a solar minimum, provide distinct contexts for assessing the predictive accuracy of our models under varied geomagnetic scenarios. This selection allowed for a comprehensive analysis of the differential effects of high versus low solar activity on ionospheric scintillation, thereby enriching our understanding of ionospheric dynamics across different solar cycles. The objective was to predict ionospheric scintillation for January, March, and July of the years 2015 and 2020. These GNSS stations recorded data at 15 and 30-s intervals. The distribution of these stations is depicted in Figure 4. To mitigate tracking errors such as multipath, a satellite elevation cutoff angle of 20 degrees was implemented. The parameters at the Ionospheric Piercing Point (IPP) altitude were set to 450 km [40]. To illustrate the spatial distribution of ionospheric scintillation over South America, two-dimensional (2D) S4 maps were generated. These maps were compiled by averaging all available IPP S4 values within a 1\({}^{\circ}\) latitude by 1\({}^{\circ}\) longitude bin, across a 5-min interval. Figure 4 illustrates an example of a raw S4 map observed on IPP, where Kriging interpolation was utilized to fill in bins devoid of S4 data, resulting in the completed S4 map also depicted in Figure 4. The dimensions of this filled S4 map are 70 in height (latitude) and 50 in width (longitude), with a grid size of 1 \(\times\) 1\({}^{\circ}\). This interpolation technique was applied to the raw 2D S4 maps derived from the GNSS data collected in January, March, and July of 2015 (maximum solar activity) and 2020 (minimum solar activity). Figure 4 illustrates the geographical dispersion of GNSS stations in South America, with a notable paucity in the central region of Brazil. Conversely, the peripheries and coastal areas of the continent demonstrate a heightened concentration of GNSS stations. This distribution pattern is partly attributed to the application of GNSS receiver data in monitoring oceanic events, including tsunamis. To mitigate the limitations posed by this uneven station distribution, the Kriging algorithm was implemented for effective data interpolation in this study.