## 7 Conclusions
Our analysis of the performance metrics--CC, RMSE, and ME--across various testing intervals revealed that the ConvGRU-LC model consistently outperformed the ConvGRU-L\({}_{\text{log-cosh}}\) and ConvGRU-L\({}_{\text{S}}\) models. In 2015, the ConvGRU-L\({}_{\text{C}}\) model achieved a CC of 0.91 in January and March, which slightly improved to 0.92 in July. In comparison, the ConvGRU-L\({}_{\text{log-cosh}}\) model maintained a CC of about 0.89 throughout the same year, whereas the ConvGRU-L\({}_{\text{S}}\) model lagged with a CC of 0.78 in July, marking it the lowest among the models. This shows a clear superiority of the ConvGRU-L\({}_{\text{C}}\) model in handling complex ionospheric data with more accuracy. Moreover, the ConvGRU-L\({}_{\text{C}}\) model not only excelled in correlation coefficient but also showed significantly better performance in terms of RMSE and ME. Moreover, our detailed examination of performance metrics across the ConvGRU models, particularly focusing on statistical parameters such as FAR, CSI, POD, and TSS presented clear distinctions in model efficacy. Data from 2015 and 2020, spanning January, March, and July revealed that the ConvGRU-L\({}_{\text{C}}\) model consistently outperformed its counterparts. For instance, in January 2020, ConvGRU-L\({}_{\text{C}}\) achieved a lower FAR of 0.19 compared to 0.20 for ConvGRU-L\({}_{\text{log-cosh}}\), marking a 5% improvement. Additionally, ConvGRU-L\({}_{\text{C}}\) exhibited a POD of 0.80, demonstrating about 2.56% better detection capability than the 0.78 ConvGRU-L\({}_{\text{log-cosh}}\). Further comparison between ConvGRU-L\({}_{\text{C}}\) and ConvGRU-L\({}_{\text{S}}\) shows more pronounced differences; ConvGRU-L\({}_{\text{C}}\)'s FAR of 0.19 in January 2020 improved by about 9.52% over ConvGRU-L\({}_{\text{S}}\)'s FAR of 0.21. The TSS for ConvGRU-L\({}_{\text{C}}\) was also superior, recording 0.72 compared to 0.69 for ConvGRU-L\({}_{\text{S}}\)--a 4.35% enhancement. When ConvGRU-L\({}_{\text{log-cosh}}\) was compared with ConvGRU-L\({}_{\text{S}}\), the former generally presented better performance metrics, such as a CSI of 0.68 in July 2020, approximately 3.03% higher than the 0.66 for ConvGRU-L\({}_{\text{S}}\). These findings underscore the ConvGRU-L\({}_{\text{C}}\) model's robust adaptability and precision in modeling ionospheric scintillations, particularly advantageous under varying geomagnetic and solar conditions, evidenced by its lower false alarm rates and higher overall skill scores. In conclusion, the integration of ConvGRU networks in this study signifies a pivotal advancement in the predictive modeling of ionospheric disturbances, suggesting potential for the development of real-time or near-real-time monitoring systems. Future research will aim to further refine these models, broaden their geographical applicability, and integrate more sophisticated atmospheric data inputs to elevate the accuracy and reliability of predictions. This progression is essential for mitigating the impacts of space weather on contemporary technological systems and for fostering the advancement of global satellite communication networks. Methodology, A.A.; software, A.A.; investigation, A.A.; data curation, A.A.; writing--original draft, A.A.; writing--review and editing, A.A., I.J., M.A. and R.B.L. ; supervision, I.J., M.A. and R.B.L. ; project administration, I.J., M.A. and R.B.L. ; funding acquisition, R.B.L. All authors have read and agreed to the published version of the manuscript. The Article Processing Charges (APC) were funded by the Natural Sciences and Engineering Research Council of Canada (NSERC). The GNSS data received from the network of stations with a 121-observation rate can be downloaded from the UNAVCO website at [[https://www.unavco.org/data/gps-gnss/gps-gnss.html](https://www.unavco.org/data/gps-gnss/gps-gnss.html)]([https://www.unavco.org/data/gps-gnss/gps-gnss.html](https://www.unavco.org/data/gps-gnss/gps-gnss.html)).