## 6 Evaluation of Results  
In the evaluation section of this scientific article, we examine the performance of ConvGRU models (ConvGRU-L\({}_{\rm C}\), ConvGRU-L\({}_{\rm log-cosh}\), and ConvGRU-L\({}_{\rm S}\)) in predicting ionospheric scintillation, treating it as a classification problem. To assess the models' capabilities, we categorize ionospheric disturbances into four distinct levels based on the S4 index values, which are indicative of scintillation intensity. These levels are defined as: no scintillation (S4 \(<\) 0.2), weak scintillation (0.2 \(\leq\) S4 \(<\) 0.4), moderate scintillation (0.4 \(\leq\) S4 \(<\) 0.6), and strong scintillation (S4 \(\geq\) 0.6). The thresholds for classification are set at S4 values of 0.2, 0.4, and 0.6, assigning a label '1' to records exceeding these thresholds and '0' otherwise. We employ several statistical parameters to evaluate the predictive accuracy and reliability of these models, including the False Alarm Rate (FAR), Critical Success Index (CSI), Probability of Detection (POD), and True Skill Score (TSS). The FAR measures the proportion of false positives relative to the total number of actual negative cases. It is calculated as the number of false positives divided by the sum of false positives and true negatives. The CSI, also known as the Threat Score, evaluates the accuracy of predictions by considering both hits (true positives) and misses (false negatives), as well as false alarms (false positives). The POD assesses the model's ability to correctly identify actual positives, calculated as the ratio of true positives to the sum of true positives and false negatives. The TSS compares the hit rate and the false alarm rate to gauge the model's ability to discriminate between positive and negative classes beyond random chance. The FAR, CSI, POD, and TSS are calculated in the following manner:  
\[FAR=\frac{FP}{TP+FP} \tag{28}\]  
\[CSI=\frac{TP}{TP+FP+FN} \tag{29}\]  
\[POD=\frac{TP}{TP+FN} \tag{30}\]  
\[TSS=\frac{TP}{TP+FN}-\frac{FP}{FP+TN} \tag{31}\]  
where TP, TN, FP, and FN represent the numbers of true positives, true negatives, false positives, and false negatives, respectively. A lower FAR indicates fewer incorrect predictions of scintillation when it does not occur, signifying higher model precision. The CSI index does not take true negatives into account, focusing solely on the cases predicted as events. High POD values indicate the effective detection of scintillation events, critical for ensuring reliability in practical applications. The TSS ranges from \(-\)1 to 1, where 1 signifies perfect skill, 0 no skill (equivalent to random chance), and \(-\)1 indicates total discordance [67; 68; 69; 70]. Table 3 provides a detailed comparative analysis of the performance metrics for the ConvGRU-LConvGRU-L\({}_{\text{Log-cosh}}\), and ConvGRU-L\({}_{\text{S}}\) models applied to the test dataset. Table 3 includes skill scores for January, March, and July across the years 2015 and 2020, covering various conditions of ionospheric irregularities. This systematic evaluation aids the understanding of the effectiveness of each model under different temporal and environmental conditions, highlighting their predictive accuracies during periods of ionospheric scintillation. In this detailed analysis, we delve into the performance metrics of the ConvGRU-L\({}_{\text{C}}\), ConvGRU-L\({}_{\text{Log-cosh}}\), and ConvGRU-L\({}_{\text{S}}\) models applied to predict ionospheric scintillation over South America during January and March of 2015 and 2020. The assessment utilizes statistical parameters such as the False Alarm Rate (FAR), Critical Success Index (CSI), Probability of Detection (POD), and True Skill Score (TSS) to comprehensively evaluate the models' accuracy across these periods characterized by differing solar activity levels. The ConvGRU-LC model exhibited notable improvements in all metrics from 2015 to 2020.