## 5 Conclusions  
Developing accurate models to predict ionospheric scintillations is crucial for mitigating the adverse effects of this phenomenon on satellite communication and navigation systems. In this work, machine learning techniques, specifically the Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) algorithms, were employed to predict ionospheric scintillation events across the equatorial anomaly, incorporating ULF wave power and solar wind parameters. The key findings and conclusions from this study are summarized below:  
1. A significant correlation was observed between Pc5 pulsations, scintillation events, and the Kp index. This finding confirms that Pc5 waves, driven by geomagnetic activities, are likely contributors to ionospheric scintillations. The correlation supports previous studies that identify the Kp index as a valuable metric for studying ULF wave phenomena. 2. On a geomagnetically quiet day (22 March 2016), a high-amplitude scintillation event followed the observation of a high-amplitude Pc5 wave at the Bear Island (BJN) station. This temporal sequence suggests that Pc5 waves may trigger scintillation events, emphasizing the importance of including such wave characteristics in model training. 3. The integration of Pc5 wave characteristics into the machine learning models represents a novel approach that has rarely been explored in prior research. The models were trained using solar wind parameters (IMF, \(B_{z\_GSE}\), \(B_{y\_GSE}\), solar wind speed, solar wind pressure, \(E_{y}\), and F10.7) alongside temporal features such as year, day of the year (DOY), and hour. This comprehensive methodology accounted for diurnal variations and long-term trends of scintillations throughout the solar cycle, thereby enhancing prediction accuracy. The study presented a multidimensional predictive framework that is more robust than traditional methods that focus solely on solar and geomagnetic indices. * Two machine learning models, RF and XGBoost, were developed to predict scintillation events. The models were tested on unseen data, and the XGBoost model achieved a higher \(R^{2}\) score of 0.74 compared to 0.66 for the RF model. Both models had similar RMSE values of 0.01, indicating comparable error rates. However, XGBoost outperformed RF due to its gradient-boosting mechanism, which refines predictions more effectively. A key strength of this study lies in the use of multiple machine learning models to analyze the relationships between ULF waves, solar wind parameters, and ionospheric scintillation. However, a limitation is the relatively moderate \(R^{2}\) scores, which suggest further refinement of the model inputs and hyperparameters may be required to achieve higher predictive accuracy. Future work could explore additional features or higher temporal resolution data to enhance model performance. * Based on the comparative performance of the RF and XGBoost models, we recommend ensemble learning techniques for predicting ionospheric scintillations. The use of gradient-boosting algorithms, in particular, can capture complex relationships in the data and produce more accurate predictions, as demonstrated by the XGBoost model in this study. The novelty of this work lies in its integration of ULF wave characteristics, particularly Pc5 waves, with machine learning models to predict ionospheric scintillation. While previous studies have established correlations between geomagnetic activity and ULF waves, few have incorporated Pc5 wave features into predictive models for scintillation. Our approach using ensemble learning techniques, coupled with multidimensional input parameters including ULF waves and solar wind parameters offers a more robust, data-driven method for scintillation prediction. The analysis of a geomagnetically quiet day further enhances the clarity of the relationship between Pc5 waves and scintillations, contributing to a more nuanced understanding of their role as potential triggers.