## 3 Results and Discussion
### Evaluation of the Machine Learning Models
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multicolumn{1}{c}{**Model**} & \multicolumn{1}{c}{**Input Scenario**} & \multicolumn{1}{c}{\(\mathbf{R}^{2}\)} & \multicolumn{1}{c}{**Fitting Equation**} \\ \hline RF & & Sc1 & 0.9662 & y = 0.8619x + 2.581 \\  & & Sc2 & 0.9621 & y = 0.8532x + 2.604 \\  & & Sc3 & 0.2407 & y = 0.222x + 16.925 \\  & & Sc4 & 0.9680 & y = 0.8633x + 2.49 \\  & & Sc5 & 0.9680 & y = 0.8633x + 2.49 \\ \hline XGB & & Sc1 & 0.9671 & y = 0.8757x + 1.7511 \\  & & Sc2 & 0.9688 & y = 0.8759x + 1.7894 \\  & & Sc3 & 0.2322 & y = 0.3305x + 16.37 \\  & & Sc4 & 0.9618 & y = 0.874x + 1.7483 \\  & & Sc5 & 0.9499 & y = 0.8541x + 2.1941 \\ \hline SVR & & Sc1 & 0.9816 & y = 0.8826x + 1.503 \\  & & Sc2 & 0.9802 & y = 0.8798x + 1.5772 \\  & & Sc3 & 0.2390 & y = 0.9959x + 14.929 \\  & & Sc4 & 0.9846 & y = 0.8807x + 1.7732 \\  & & Sc5 & 0.9730 & y = 0.8647x + 1.8714 \\ \hline RF-XGB & & Sc1 & 0.9727 & y = 0.8873x + 1.8993 \\  & & Sc2 & 0.9625 & y = 0.8943x + 1.9451 \\  & & Sc3 & 0.2528 & y = 0.1897x + 15.18 \\  & & Sc4 & 0.9683 & y = 0.8743x + 2.2113 \\  & & Sc5 & 0.9601 & y = 0.8829x + 2.1468 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The coefficient of determination between actual and predicted GWFP values and the best-fitting equation. \begin{table}
\begin{tabular}{l l c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Model**}} & \multirow{2}{*}{**Index**} & \multicolumn{5}{c}{**Input Scenario**} \\ \cline{3-6}  & & **Sc1** & **Sc2** & **Sc3** & **Sc4** & **Sc5** \\ \hline RF & \multicolumn{1}{c}{MBE (m\({}^{3}\) ton\({}^{-1}\))} & 0.48 & 0.65 & 0.34 & 0.54 & 0.57 \\  & \multicolumn{1}{c}{MAPE} & 2.58 & 1.29 & 120.97 & 1.48 & 1.51 \\  & \multicolumn{1}{c}{MAPE} & 1.26 & 0.63 & 59.28 & 0.73 & 0.74 \\ \hline XGB & \multicolumn{1}{c}{MBE (m\({}^{3}\) ton\({}^{-1}\))} & 1.01 & 0.96 & \(-1.51\) & 1.05 & 1.05 \\  & \multicolumn{1}{c}{MAPE} & \(-6.28\) & \(-6.83\) & 118.00 & \(-8.05\) & \(-1.56\) \\  & \multicolumn{1}{c}{MAPE} & \(-3.08\) & \(-3.35\) & 57.82 & \(-3.95\) & \(-0.76\) \\ \hline SVR & \multicolumn{1}{c}{MBE (m\({}^{3}\) ton\({}^{-1}\))} & 1.10 & 1.09 & 5.14 & 0.87 & 1.13 \\  & \multicolumn{1}{c}{MAPE} & \(-8.14\) & \(-8.30\) & 98.31 & \(-2.36\) & 1.06 \\  & \multicolumn{1}{c}{MAPE} & \(-3.99\) & \(-4.07\) & 48.17 & \(-1.16\) & 0.52 \\ \hline RF-XGB & \multicolumn{1}{c}{MBE (m\({}^{3}\) ton\({}^{-1}\))} & 0.98 & 1.17 & 1.87 & 0.75 & 1.09 \\  & \multicolumn{1}{c}{MAPE} & \(-2.58\) & \(-1.53\) & 92.66 & 3.78 & \(-1.35\) \\  & \multicolumn{1}{c}{MAEE} & \(-1.26\) & \(-0.75\) & 45.40 & 1.85 & \(-0.66\) \\ \hline RF-SVR & \multicolumn{1}{c}{MBE (m\({}^{3}\) ton\({}^{-1}\))} & 0.03 & \(-0.06\) & 5.05 & 0.22 & 0.15 \\  & \multicolumn{1}{c}{MAPE} & \(-1.83\) & \(-1.12\) & 64.53 & \(-0.72\) & 0.25 \\  & \multicolumn{1}{c}{MAPE} & \(-0.90\) & \(-0.55\) & 31.62 & \(-0.35\) & 0.12 \\ \hline XGB-SVR & \multicolumn{1}{c}{MBE (m\({}^{3}\) ton\({}^{-1}\))} & 0.33 & 0.48 & 2.86 & 0.06 & 0.26 \\  & \multicolumn{1}{c}{MAPE} & 0.06 & \(-1.13\) & 60.44 & 5.48 & 0.87 \\  & \multicolumn{1}{c}{MAPE} & 0.03 & \(-0.55\) & 29.61 & 2.68 & 0.43 \\ \hline RF-XGB-SVR & \multicolumn{1}{c}{MBE(m\({}^{3}\) ton\({}^{-1}\))} & 0.97 & 1.32 & 2.44 & 1.25 & 0.88 \\  & \multicolumn{1}{c}{MAPE} & 3.61 & \(-0.47\) & 99.89 & \(-0.71\) & 5.16 \\  & \multicolumn{1}{c}{MAPE} & 1.77 & \(-0.23\) & 48.94 & \(-0.35\) & 2.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance statistics of ML models applied to the five distinct climate and remote sensing variable scenarios. During the test period, bar charts were utilized to compare the ML models, whether single or hybrid, and scenarios based on the CC, NSE, MAE, and Tstat criteria. These comparisons were made separately for each scenario. The comparison charts, also known as combo bar charts, revealed significant variations in the values of CC. The analysis indicated that the highest CC value of 0.99 was observed under Sc1 and Sc4 for SVR, RF-XGB, RF-SVR, XGB-SVR, and RF-XGB-SVR, surpassing both XGB and RF with a CC value of 0.98.