## 9 Comparison and Evaluation of the Developed Models  
After the models were developed and HL of EEB systems was predicted, their results were compared and evaluated together through the performance metrics (e.g., RMSE, R\({}^{2}\), and MAE), and the intensity of color and ranking methods. A comprehensive assessment of the developed models based on both training and the testing dataset was conducted in this section. Table 2 presents the prediction results of HL by the hybrid intelligent techniques (i.e., GA-ANN, ABC-ANN, PSO-ANN, and ICA-ANN), and their performance in the training process.  
From Table 2, the color intensity revealed that the GA-ANN model provided the most dominant performance in the training process. It obtained the lowest error with an RMSE of 1.701, R\({}^{2}\) of 0.972, and MAE of 0.784, and the total ranking of 10, on the training dataset. The ABC and PSO meta-heuristics algorithms yielded lower performance in the optimization of the ANN model in the training process, with RMSE of 1.833, 1.822; R\({}^{2}\) of 0.927, 0.972; MAE of 0.813, 0.872, and the total ranking of 7, and 6, respectively. The weakest model in this optimization process is the ICA-ANN model with an RMSE of 1.847, R\({}^{2}\) of 0.971, MAE of 0.860, and the total ranking of 4. To have a complete conclusion, the models' performances were assessed on the testing dataset, where the dataset was considered as the new data and ever not used in the training process. Table 3 shows the results and the performance of the models in the testing process.  
Based on the reports of Table 3, similar results to the training process were reflected. The color intensity of the red color indicated that the GA-ANN model was the best model in a comparison of  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Model** & **RMSE** & **R\({}^{2}\)** & **MAE** & **Rank for RMSE** & **Rank for R\({}^{2}\)** & **Rank for MAE** & **Total Ranking** \\ \hline GA-ANN & 1.701 & 0.972 & 0.784 & 4 & 2 & 4 & 10 \\ PSO-ANN & 1.822 & 0.992 & 0.872 & 3 & 2 & 1 & 6 \\ ICA-ANN & 1.847 & 0.971 & 0.860 & 1 & 1 & 2 & 4 \\ ABC-ANN & 1.833 & 0.992 & 0.813 & 2 & 2 & 3 & 7 \\ \hline \end{tabular}
\end{table}
Table 2: Prediction results of the hybrid models and their performance (for the training process).  
Figure 21: HL predictions on the testing dataset of the ABC-ANN model.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Model** & **RMSE** & **R\({}^{2}\)** & **MAE** & **Rank for RMSE** & **Rank for R\({}^{2}\)** & **Rank for MAE** & **Total Ranking** \\ \hline GA-ANN & 1.625 & 0.980 & 0.798 & 4 & 4 & 4 & 12 \\ PSO-ANN & 1.932 & 0.972 & 1.027 & 2 & 2 & 1 & 5 \\ ICA-ANN & 1.982 & 0.970 & 0.980 & 1 & 1 & 2 & 4 \\ ABC-ANN & 1.878 & 0.973 & 0.957 & 3 & 3 & 3 & 3 \\ \hline \end{tabular}
\end{table}
Table 3: Prediction results of the hybrid models and their performance (for the testing process).  
the other models. The corresponding performance values of the GA-ANN model also found with an RMSE of 1.625, R\({}^{2}\) of 0.980, MAE of 0.798, and the total ranking of 12. Whereas, the ABC-ANN, PSO-ANN, and ICA-ANN model proved lower performances, as like the training process, with RMSE of 1.878, 1.932, 1.982; R\({}^{2}\) of 0.973, 0.972, 0.970; MAE of 0.957, 1.027, 0.980; and the total ranking of 9, 5, 4, respectively.