## 3 Results and Discussion
### Evaluation of the Machine Learning Models
Following closely were Sc2, Sc4, and Sc5, with differences ranging from 0.01 to 0.02, making all scenarios, except Sc3, relatively similar. However, in the case model, the lowest CC value of 0.48 was achieved under Sc3, which took into consideration NDVI, SAVI, and NDWI (Figure 10). On the contrary, the NSE exhibited a similar pattern of data to CC, with slight discrepancies in values. The highest NSE of 0.98 was observed under Sc2 (climatic parameters) and XGB-SVR. The variance between ML models, whether single or hybrid, ranged from 0.01 to 0.03 under the same conditions, except for Sc2, where the difference between XGB-SVR and XGB was 0.05 higher for hybrid models. The lowest NSE value of 0.09 was recorded with SVR and Sc3. NSE values for Sc1, Sc2, Sc4, and Sc5 were considered very good across all models, while for Sc3, the data from all single and hybrid models were classified as satisfactory to unsatisfactory (Figure 10). Similarly, the MAE displayed a similar trend to MSE and CC, with the highest MAE of 1.00 under Sc1 (all parameters) and RF-XGB-SVR. The lowest MAE value of 0.18 was recorded with SVR and Sc3. The lowest-performing scenario across all ML models, whether single or hybrid, was Sc3. The highest Tstat value of 2.26 was observed under Sc1 (all parameters) and the SVR model. Conversely, the lowest Tstat value of 0.06 was recorded with RF-SVR and Sc1 (Figure 10). Scenarios Sc4 and Sc5 were found to be favourable for all models compared to Sc1 and Sc2 due to having fewer parameters, high accuracy, and low errors, which closely resembled Sc1 and Sc2. Therefore, if remote sensing images are available, weather stations are not updated, and only Peeff, Tmax, Tmin, and SA can be measured, it would be satisfactory to achieve accurate predictions for GWFP, especially in low-income countries. The performance of machine learning models was assessed across five different climate and remote sensing scenarios. However, a distinct evaluation of climate change (CC) and scenario criteria was obtained for the remaining scenarios. Various metrics were employed to gauge the accuracy of the scenarios, aiming to extract more relevant insights from the time series data. Scenario model accuracy refers to the percentage of accurate predictions for the test data, indicating how closely the actual values align with the predicted values obtained through the specific scenario and prediction model. Achieving accuracy levels \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **Input Scenario** & \(\mathbf{R^{2}}\) & **Fitting Equation** \\ \hline RF-SVR & Sc1 & 0.971 & y = 0.9155x + 1.8096 \\  & Sc2 & 0.9668 & y = 0.91555x + 1.8931 \\  & Sc3 & 0.2811 & y = 0.1536x + 13.344 \\  & Sc4 & 0.9714 & y = 0.8969x + 2.022 \\  & Sc5 & 0.9692 & y = 0.9013x + 1.9928 \\ \hline XGB-SVR & Sc1 & 0.9781 & y = 0.9203x + 1.3998 \\  & Sc2 & 0.9809 & y = 0.9198x + 1.2626 \\  & Sc3 & 0.3409 & y = 0.2536x + 13.368 \\  & Sc4 & 0.9782 & y = 0.9094x + 1.907 \\  & Sc5 & 0.9683 & y = 0.9167x + 1.5544 \\ \hline RF-XGB-SVR & Sc1 & 0.9593 & y = 0.7911x + 3.4766 \\  & Sc2 & 0.9555 & y = 0.7801x + 3.3624 \\  & Sc3 & 0.4960 & y = 0.3492x + 11.399 \\  & Sc4 & 0.9549 & y = 0.785x + 3.3223 \\  & Sc5 & 0.9487 & y = 0.777x + 3.8615 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._above 90% is considered satisfactory. In some scenarios and models, values exceeding 1 were noted, suggesting that the prediction outcomes might be influenced by random errors, which could be attributed to measurement errors rather than systematic factors. Particularly, RF and XGB models showed overestimated scenario values, which could be attributed to random errors. Figure 10: _Cont._  
Radar charts were utilized to illustrate the RMSE of the GWFP for three different models, both single and hybrid, across five scenarios (Figure 11). The highest RMSE value in Sc1 was recorded with the RF model at 4.28, followed by XGB at 4.21, while the lowest RMSE values were seen with the XGB-SVR hybrid models at 3.42.