## 3 Case Study Results
### Metropolitan Lima Stations
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c} \hline \multicolumn{1}{c}{**Station**} & \multicolumn{4}{c}{**Case \& \multicolumn{4}{c}{**San Bois**}} & \multicolumn{4}{c}{**Santa Bois**} & \multicolumn{4}{c}{**Santa Bois**} \\ \cline{3-14} \multicolumn{1}{c}{**S.N**} & **Model** & **RMSE** & **RMSE** & **RMSE** & **RMSE** & **RMSE** & **RMSE** & **MAE** & **MAE** & **CC** & **RMSE** & **RMSE** & **RMSE** & **RMSE** & **RMSE** & **RMSE** & **RMSE** & **RMSE** & **RMSE** & **RMSE** & **CC** \\ \hline
1 & **STD1** & 5.599 & 5.414 & 2.399 & 2.897 & 0.952 & 5.073 & 16.53 & 3.399 & 22.58 & 0.952 & 2.115 & 2.60 & 1.307 & 1.217 & 0.958 & 5.299 & 4.084 & 3.916 & 3.916 & 3.916 \\
2 & **STD1** & 5.699 & 4.528 & 2.06 & 1.805 & 0.921 & 5.145 & 14.06 & 3.334 & 2.563 & 0.955 & 2.081 & 2.477 & 1.567 & 0.976 & 5.338 & 4.020 & 3.909 & 186.86 & 3.915 & 3.916 \\
3 & **STD1** & 4.055 & 4.059 & 1.502 & 1.762 & 0.807 & 4.899 & 11.316 & 2.396 & 0.969 & 1.974 & 2.476 & 1.488 & 10.297 & 0.979 & 5.244 & 3.768 & 3.858 & 178.93 & 3.937 \\
4 & **STD1** & 5.621 & 4.056 & 1.714 & 1.682 & 0.529 & 4.971 & 16.53 & 3.088 & 2.116 & 0.953 & 1.573 & 2.538 & 1.499 & 1.704 & 0.979 & 5.310 & 3.042 & 3.907 & 172.80 & 3.917 \\
5 & **STD1** & 5.519 & 4.044 & 2.739 & 1.822 & 0.952 & 4.777 & 16.64 & 2.388 & 2.128 & 0.956 & 1.715 & 2.533 & 1.499 & 1.704 & 0.979 & 5.310 & 3.042 & 3.907 & 176.20 & 3.917 \\
6 & **STD1** & 5.519 & 4.044 & 2.739 & 1.822 & 0.952 & 3.775 & 16.62 & 2.388 & 2.248 & 0.956 & 1.115 & 1.729 & 1.727 & 0.956 & 3.277 & 0.956 & 3.277 & 4.011 & 3.907 & 3.042 & 3.907 & 3.042 \\
7 & **STD1** & 5.699 & 4.635 & 2.06 & 2.06 & 8.052 & 6.121 & 2.744 & 2.948 & 2.943 & 0.963 & 2.081 & 2.447 & 1.357 & 1.887 & 0.977 & 3.077 & 3.041 & 3.041 & 3.903 & 160.92 & 3.915 & 3.915 \\
9 & **STD1** & 4.675 & 4.655 & 1.933 & 1.825 & 0.947 & 3.746 & 1.188 & 2.406 & 2.086 & 0.980 & 1.885 & 1.853 & 1.952 & 0.927 & 0.956 & 3.058 & 3.336 & 2.360 & 2.391 & 3.915 & 0.956 \\
10 & **STD1** & 5.647 & 5.315 & 2.277 & 2.175 & 0.835 & 0.845 & 1.867 & 3.097 & 2.847 & 0.949 & 2.213 & 2.872 & 1.944 & 1.730 & 1.730 & 0.973 & 3.043 & 3.917 & 3.947 & 3.947 & 3.945 \\
11 & **STD1** & 5.700 & 4.845 & 2.06 & 1.770 & 0.822 & 5.957 & 1.956 & 3.076 & 2.848 & 0.957 & 2.221 & 2.711 & 1.860 & 1.899 & 0.973 & 3.075 & 3.074 & 3.979 & 3.914 & 3.914 & 3.914 \\
12 & **STD1** & 5.709 & 4.895 & 2.06 & 1.727 &From all twenty-seven models, in each monitoring station, the best four hybrid combination models are selected for comparison and compared with other models in each case. The outcome of all these best hybrid combination models is tabulated in Table 4. For example, in the case of the ATE station, based on the performance accuracy measure findings, it is evident that the \({}^{\textbf{a}}\textbf{STLD}_{\textbf{c}}^{\textbf{b}}\) give the least values (RMSE = 4.611, RMSPE = 4.464, MAE = 1.711, MAPE = 14.862, and CC = 0.949). Therefore, it is concluded that the \({}^{\textbf{a}}\textbf{STLD}_{\textbf{c}}^{\textbf{b}}\) is the best model among the best models as well as all twenty-seven models. In the same way, in the case of the CDM station, from Table 4, it is confirmed that the \({}^{\textbf{b}}\textbf{STLD}_{\textbf{c}}^{\textbf{c}}\) produced the smallest values (RMSE = 3.637, RMSPE = 11.846, MAE = 2.356, MAPE = 20.441, and CC = 0.978). Hence, it is concluded that the \({}^{\textbf{b}}\textbf{STLD}_{\textbf{c}}^{\textbf{c}}\) is the best model among the best models as well as all twenty-seven models. However, in the case of the SB station results, it is evident that the \({}^{\textbf{c}}\textbf{STLD}_{\textbf{c}}^{\textbf{b}}\) produced the smallest values (RMSE=1.969, RMSPE = 15.924, MAE = 1.462, MAPE = 76.261, and CC = 0.989) within the final best hybrid combination models. Thus, it is concluded that the \({}^{\textbf{c}}\textbf{STLD}_{\textbf{c}}^{\textbf{b}}\) is the best hybrid combination model among the best models as well as all twenty-seven models.