*3.3. Comparison of Training and Testing Datasets for Scenario 2*

In Scenario 2, 70% of the entire data set has been used for training, and the rest of the data has been used for testing the developed model. The training results obtained by ANN, Wavelet, and SVM have been shown in Table 5.

As shown in Table 5, among three developed ANN models, the ANN-1 has the highest PCC value of 0.760, the lowest RMSE value of 1.180, the highest NSE value of 0.577, and the highest WI value of 0.854. Similarly, for the WANN model, WANN-2 has shown better performance with a PCC value of 0.725, a lowest RMSE value of 1.264, a highest NSE value of 0.515, and a highest WI value of 0.831. Furthermore, among developed SVM-RF and SVM-LF models, SVM-RF-3 has shown better performance than other developed models. The SVM-RF-3 model has the highest PCC value of 0.812, the lowest RMSE value of 1.262, the highest NSE value of 0.650, and the highest WI value of 0.714 during training datasets. The values of PCC, RMSE, NSE, and WI for MLR techniques were 0.693, 1.308, 0.481, and 0.799, respectively, during training processes. Thus, it can be stated that SVM-RF has modeled the Epan most efficiently among all the machine-learning algorithms developed for training.

**Figure 6.** *Cont*.

**Figure 6.** Line and scatter plot between observed and predicted data at Scenario 1 for (**a**) ANN, (**b**) WANN (**c**) SVM-RF, (**d**) SVM-LF, and (**e**) MLR for the study area.

For Scenario 2, where 30% of the data set has been used for testing, model ANN-1 has the highest PCC value of 0.547, the lowest RMSE value of 1.222, the highest NSE value of 0.046, and a WI value of 0.704 among ANN models. Similarly, WANN-1 has shown better performance, with a PCC value of 0.457, the lowest RMSE value of 1.252, the highest NSE value of −0.002, and the highest WI value of 0.639 WANN models. Furthermore, SVM-RF-3 has shown better performance as compared to other developed models among SVM-RF and SVM-LF models. The SVM-RF-3 model has the highest PCC value of 0.568, the lowest RMSE value of 1.262, and the highest WI value of 0.714 during training datasets. The values of PCC, RMSE, NSE, and WI for MLR techniques were 0.531, 1.262, −0.017, and 0.700, respectively. The scatter plot and line diagram for testing have been shown in Figure 7. It can be seen from the line diagram that the obtained results were under-predicted for all models. The scatter plot showed that the highest value of the coefficient of determination (R2) was obtained for SVM-RF models of 0.3221. Thus, it can be shown that SVM-RF has modeled the Epan most efficiently among all the machine learning algorithms developed for testing.

**Figure 7.** Line and scatter plots between observed and predicted data at Scenario 2 for (**a**) ANN, (**b**) WANN (**c**) SVM-RF, (**d**) SVM-LF, and (**e**) MLR, for the study area.


**Table 5.** Results for ANN, WANN, SVM-RF, SVM-LF, and MLR during training and testing period for Scenario 2 (70–30: Training–Testing).
