## 5 Results and Discussion
### On the Variability of Model Performance when Grouped by the Considered Scenarios  
The results of the evaluation using the subsampling method proposed in Section 4.5 are presented here. Tables 4-6 show the means and standard deviations of the metrics obtained for each of the stations, across all scenarios and models. In Scenario 1 (Table 4), the ST_CNN_v2 model performs consistently better for most stations, showing low MAE and RMSE values and high R2 score values. The ST_CNN_v1 model generally performs slightly worse, with higher MAE and RMSE values and lower R2 score values compared to the other models. Although the ST_Dilated_CNN model differs between 0.01 and 0.04 in mean R2 scores with respect to ST_CNN_v2, it maintains a consistent performance. \begin{table}
\begin{tabular}{c l c c c c c c} \hline \hline \multirow{2}{*}{**Station**} & \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**MAE**} & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{**R2 Score**} \\ \cline{3-8}  & & **Mean** & **Std. Deviation** & **Mean** & **Std. Deviation** & **Mean** & **Std. Deviation** \\ \hline \multirow{3}{*}{BU04} & ST\_CNN\_v1 & 73.95 & 11.69 & 106.98 & 19.43 & 0.8 & 0.03 \\  & ST\_CNN\_v2 & 41.03 & 7.42 & 66.54 & 13.19 & 0.92 & 0.02 \\  & ST\_Dilated\_CNN & 62.11 & 8.9 & 82.07 & 14.82 & 0.88 & 0.02 \\ \hline \multirow{3}{*}{LE05} & ST\_CNN\_v1 & 62.68 & 1.87 & 89.1 & 2.38 & 0.87 & 0.04 \\  & ST\_CNN\_v2 & 38.26 & 1.52 & 60.65 & 4.0 & 0.94 & 0.01 \\  & ST\_Dilated\_CNN & 48.42 & 6.65 & 65.58 & 7.7 & 0.93 & 0 \\ \hline \multirow{3}{*}{P03} & ST\_CNN\_v1 & 61.96 & 7.37 & 93.68 & 14.24 & 0.86 & 0.02 \\  & ST\_CNN\_v2 & 47.6 & 5.75 & 71.46 & 10.15 & 0.92 & 0.01 \\ \cline{1-1}  & ST\_Dilated\_CNN & 74.54 & 8.77 & 95.01 & 13.83 & 0.85 & 0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Mean and standard deviation values of the performance metrics obtained in Scenario 1. In Scenario 2 (Table 5), the response of the models remains the same, with ST_CNN_V2 and ST_Dilated_CNN having the best metrics. For example, at station BU04, ST_CNN_v2 has an MAE of 50.28 \(\pm\) 7.04, an RMSE of 75.91 \(\pm\) 11.97, and an R2 score of 0.90 \(\pm\) 0.02, outperforming ST_CNN_v1 and ST_Dilated_CNN on all metrics. These results are consistent with those presented in Section 5.2. For Scenario 3, as shown in Table 6, the ST_CNN_v2 model stands out as the best in most of the stations evaluated. For stations such as LE05 and P03, ST_CNN_v2 consistently exhibits the lowest MAE and RMSE, as well as the highest R2 score compared to the other models, indicating a better fit and predictive performance. In contrast, ST_CNN_v1 tends \begin{table}
\begin{tabular}{c l c c c c c c} \hline \hline \multirow{2}{*}{**Station**} & \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**MAE**} & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{**R2 Score**} \\ \cline{3-8}  & & **Mean** & **Std. Deviation** & **Mean** & **Std. Deviation** & **Mean** & **Std. Deviation** \\ \hline \multirow{3}{*}{P07} & ST\_CNN\_v1 & 80.77 & 11.52 & 109.86 & 17.75 & 0.77 & 0.02 \\  & ST\_CNN\_v2 & 55.31 & 10.83 & 80.5 & 18.29 & 0.88 & 0.02 \\  & ST\_Dilated\_CNN & 62.95 & 14.72 & 88.86 & 20.95 & 0.85 & 0.03 \\ \hline \multirow{3}{*}{SG01} & ST\_CNN\_v1 & 66.99 & 9.43 & 98.06 & 15.55 & 0.86 & 0.02 \\  & ST\_CNN\_v2 & 49.12 & 7.7 & 73.68 & 12.32 & 0.92 & 0.01 \\  & ST\_Dilated\_CNN & 56.24 & 6.61 & 76.46 & 11.19 & 0.91 & 0.01 \\ \hline \multirow{3}{*}{SO01} & ST\_CNN\_v1 & 128.94 & 17.55 & 168.98 & 26.34 & 0.53 & 0.13 \\  & ST\_CNN\_v2 & 104.02 & 18.54 & 138.56 & 22.69 & 0.69 & 0.07 \\  & ST\_Dilated\_CNN & 66.79 & 4.58 & 82.3 & 7.57 & 0.89 & 0.01 \\ \hline \multirow{3}{*}{ZA02} & ST\_CNN\_v1 & 64.78 & 1.76 & 89.41 & 4.46 & 0.88 & 0.02 \\  & ST\_CNN\_v2 & 45.58 & 4.31 & 61.60 & 7.09 & 0.94 & 0 \\ \cline{1-1}  & ST\_Dilated\_CNN & 49.57 & 7.53 & 68.71 & 11.08 & 0.93 & 0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._