**3. Results**

#### *3.1. Quantitative and Qualitative Evaluation of Results*

This section deals with quantitative and qualitative results obtained for the developed models. ANN and WANN trials were conducted depending on the different number of neurons in hidden layers. In contrast, SVM-LF and SVM-RF trials were performed by taking several values of SVM-g, SVM-c, and SVM-e parameters. These were represented in Tables 4–6 as a structure for the model.

#### *3.2. Comparison of Training and Testing Datasets for Scenario 1*

The training results obtained by ANN, Wavelet, and SVM have been shown in Table 4. As depicted in Table 4, for three developed ANN models, namely ANN-1, ANN-2, and ANN-3, ANN-1 has the highest PCC value of 0.832, the lowest RMSE value of 0.993, the highest NSE value of 0.685, and the highest WI value of 0.904.

Similarly, for the developed WANN model, WANN-1 has shown better performance, with a PCC value of 0.773. Furthermore, the WANN model also has the lowest RMSE value of 1.123, the highest NSE value of 0.597, and the highest WI value of 0.860. 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 0.857; it has the lowest RMSE value of 0.956, the highest NSE value of 0.708, and the highest WI value of 0.895 during training datasets. The value of PCC, RMSE, NSE, and WI for MLR techniques was 0.695, 1.274, 0.483, and 0.800. Thus, it can be stated that SVM-RF has modeled the Epan most efficiently of all the machine learning algorithms developed for training.

**Table 4.** Results for ANN, WANN, SVM-RF, SVM-LF, and M.L.R. during the training and testing period for Scenario 1 (60–40: Training–Testing).


Among developed ANN models, ANN-1 has the highest PCC value of 0.589; it has the lowest RMSE value of 1.387 and the highest NSE value of 0.136. Similarly, for the WANN model, WANN-1 has shown better performance with a PCC value of 0.505, the lowest RMSE value of 1.394, the highest NSE value of 0.129, and a WI value of 0.676.

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 0.607, RMSE value of 1.349, NSE value of 0.183, and the highest WI value of 0.749 training datasets. The values of PCC, RMSE, NSE, and WI for MLR techniques were 0.587, 1.345, 0.188, and 0.725, respectively. The scatter plot and line diagram for the testing data set has been shown in Figure 6. From the line diagram, it can be observed that the obtained results were under-predicted for all models. The scatter plot shows that the highest value of the determination (R2) coefficients was obtained for the SVM-RF model. Thus, it can be suggested that SVM-RF has modeled the Epan most efficiently among all the machine learning algorithms developed for testing.
