## 2 Materials and Methods
### Training, Testing, and Results of the ML Model
The RF algorithm demonstrated a balanced predictive behavior, underestimating 53.7% and overestimating 46.3% of the 300 test samples for the E component (Figure 4). For the N component, these percentages were 52.3% and 47.7%, respectively, indicating no systematic bias in the model's predictions. For the E component, 3.7% of the residuals exceeded \(\pm\)0.5 mm/year, with 1.3% categorized as underestimations (minimum residual: \(-\)1.1 mm/year) and 2.4% as overestimations (maximum residual: 1.25 mm/year). For the N component, 2% of the residuals exceeded \(\pm\)0.5 mm/year, with 1% representing underpredictions (minimum residual: \(-\)0.67 mm/year) and 1% representing overpredictions (maximum residual: 0.83 mm/year). Outlier analysis did not reveal any evidence of systematic error within the dataset. In addition, no correlation was observed between the reference velocity accuracies and the predicted velocity accuracies (Figure 4). The residual distribution showed that 89.3% of predictions for the E component and 93.7% for the N component fell within a \(\pm\)0.2 mm/year residual margin. This high proportion of small residuals reflects the RF model's strong predictive performance and low generalization error, indicating that the model effectively mapped the feature space to the target variable with minimal deviation. According to the performance metrics, the KNN algorithm ranks as the second most successful model after the RF algorithm (Figure 5). Considering that the training performance for the KNN algorithm was 94% for both the E and N components, and the test performance was 91% and 89%, respectively, it can be concluded that the model provides very high accuracy (Figure 5). Residuals greater than \(\pm\)0.5 mm/year account for 7.3% of the predictions for the E component and 5% for the N component. The residual values are predominantly concentrated around small negative and positive values, although larger positive residuals are observed at the extremes. For example, the highest residual is 1.73 mm/year, while the lowest is \(-\)0.99 mm/year. Despite the generally small residuals (82.7% for comp. E and 86.3% for comp. N at less than \(\pm\)0.2 mm/year), a few outliers with high residuals suggest that the model occasionally makes larger errors. This aligns with the model's 90% performance on the test data, indicating that while the model predicts correctly most of the time, it occasionally produces larger errors in some cases. Similar to the RF and KNN models, the distribution of positive and negative residuals is balanced, with no indication of systematic bias. For the E and N components, the algorithm produced residuals larger than \(\pm\)0.5 mm/year in 8% and 6.3% of the predictions, respectively. These residuals were predominantly positive (indicating overestimation), with the largest residuals being 1.40 mm/year for the E component and 1.58 mm/year for the N component. Additionally, the proportion of errors exceeding \(\pm\)1 mm/year was 4% for the E component and 3% for the N component. These larger residuals have the potential to influence the results in the studies that require high-accuracy GNSS velocity estimates. The MLR algorithm, which exhibited the lowest prediction accuracy in this study, achieved training and test scores of 72% and 71% for the E component and 76% and 72% for the N component, respectively (Table 3). These scores indicate that while MLR can reasonably explain the dependent variable, its predictive power is limited. The model produced residuals larger than \(\pm\)0.5 mm/year in 21% of the predictions for the E component and 16% for the N component (Figure 7). Additionally, the largest residual observed across all models, 2.1 mm/year, was generated by MLR. In contrast, for residuals smaller than \(\pm\)0.2 mm/year, the model performed relatively better, with 47.7% of predictions for the E component and 41.3% for the N component falling within this range. A notable limitation of the MLR model is its reduced performance when actual velocity accuracy values exceed \(\pm\)5 mm/year.