## 5 Analysis and Discussion  
During the last decade, several studies have shown possible correlations between earthquakes and TEC anomaly precursors [53; 54; 55; 63]. In this paper, we presented a new approach for testing the ability to predict earthquake events using ionospheric TEC anomalies, by applying the SVM technique with GPS ionospheric TEC time series data. The learning was applied with a pre-processed GPS ionospheric TEC time series, where the time series was extracted by a weighted average of the closest points to the earthquake's epicenter. We subtracted the moving average with a window size of 1 h from the original time series to exclude the diurnal solar periodicity effect from the TEC signal. For each earthquake, we also introduced a quiet day TEC level chosen randomly from the same day in different quiet years. Following previously reported articles, the earthquakes that we trained on were shallow (up to 60 km in depth) with large magnitudes (>6 Mw) and were less likely to have ionospheric effects than deep and small earthquakes [64; 65]. After sorting all the earthquake dates that were relevant to the available TEC maps, without any additional disturbances, we were left with a relatively small amount of data (106 samples) that the algorithm could be taught. Furthermore, by using the Bayesian optimization algorithm to obtain the best hyperparameters possible at the shortest computation time, we used a polynomial kernel function with a degree of 2, resulting in an accuracy of 82.5%, precision 85%, Recall 80%, and 65.7% in both HSS and TSS skill scores. From the ROC curve, we can deduce that the best model that we obtained yielded 19% and 61% FPR and TPR, respectively, for the training set, and 14% and 18% for both FPR and TPR, respectively, for the testing set. In addition, during the optimization process, we obtained several models that were worst than the coin toss model, resulting from the random hyperparameter picking at the start of the optimization process. To verify that the data that were taken were the data that achieved the highest accuracies, we performed an additional test that included different sets of data as follows: Group 1-contained all the earthquakes, but for each earthquake, 25 quiet days were added in order to check the accuracy of the model. Group 2-the same as before, but the earthquakes that were taken into account were those that were inland (19 events). Group 3-the earthquakes that were taken into account were those that occurred in the oceans (87 events). The following figure (Figure 10) shows a comparison of the different groups for each skill score. Group 4-contains all earthquakes and the corresponding quiet days that occurred during high solar activity (sunspot numbers > 50). We note that in group 1, the skill scores are comparable to the main group, whereas if we consider only earthquakes that were inland-group 2 (which was 18% of the overall earthquakes), we obtain much lower skill score values. For group 3, we have almost the same as group 1 scores. In addition to the fact that there are more oceanic earthquakes than inland earthquakes, it should be noted that earthquakes in the ocean are much stronger than inland earthquakes. The largest inland earthquake in the last decade (the Sichuan earthquake of 12 May 2008) was estimated at around 7.9, while the largest in-ocean events were reported in Sumatra (in 2004) and Japan (in 2011), at around 9.0-9.1. Therefore, it is difficult to perform a full comparative analysis of seismic TEC differences created by both inland and in-ocean earthquakes. Finally, for group 4, we can see that each of the skill scores dropped, which can result from the fact that, in the higher solar activity, we cannot differentiate between the different solar activities. Furthermore, a proof-of-concept demonstration is applicable, using the IGS final GIM products for a post-mission analysis; however, any future real-time earthquake event prediction platforms will require the ability to produce TEC maps at shorter latency time scales.