## 4 Results and Discussions
### Comparative Model Outputs
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Parameter**} & **Value/Description** \\ \hline Type & MLP \\ \hline Number of processing neurons & \(7\) \\ \hline Learning algorithm & BP–CG \\ \hline Processing layer activation function & Hyperbolic \\ \hline Output layer activation function & Logistic \\ \hline \hline \end{tabular}
\end{table} 
Table 3: ANN Model. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Accuracy Parameter**} & \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Dataset**} \\ \cline{3-4}  & & **Training** & **Test** \\ \hline \multirow{3}{*}{MSE (kPa/m)} & Trial 1 & 0.52 & 0.46 \\ \cline{2-4}  & Trial 2 & 0.55 & 0.44 \\ \cline{2-4}  & Trial 3 & 0.56 & 0.40 \\ \hline \multirow{3}{*}{R-square} & Trial 1 & 0.58 & 0.56 \\ \cline{2-4}  & Trial 2 & 0.62 & 0.55 \\ \cline{2-4}  & Trial 3 & 0.61 & 0.49 \\ \cline{2-4}  & Trial 1 & 35 & 58 \\ \cline{2-4}  & Trial 2 & 39 & 61 \\ \cline{2-4}  & Trial 3 & 35 & 59 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of Accuracy Parameters for Cross-Validation of MLR. Figure 5: Comparison of MSE. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Accuracy Parameter**} & \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Dataset**} \\ \cline{3-4}  & & **Training** & **Test** \\ \hline \multirow{3}{*}{MSE (kPa/m)} & SVM & 0.24 & 0.28 \\ \cline{2-4}  & ANN & 0.03 & 0.04 \\ \cline{2-4}  & MLR & 0.74 & 0.66 \\ \hline \multirow{3}{*}{R-square} & SVM & 0.83 & 0.83 \\ \cline{2-4}  & ANN & 0.98 & 0.98 \\ \cline{2-4}  & MLR & 0.61 & 0.53 \\ \hline \multirow{3}{*}{MAPE (\%)} & SVM & 61 & 68 \\ \cline{2-4}  & ANN & 16 & 20 \\ \cline{2-4}  & MLR & 38 & 59 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of Accuracy Parameters. The comparison of accuracy measures presented in Figures 6 and 7 demonstrates that the ANN model performs much better than the other models, providing the least MSE and MAPE, and highest CC. Also, all models have shown negligible differences between training and test datasets in terms of MSE and CC values. However, the difference in MAPE was very significant for SVM and MLR while it was very low for ANNs. This could be an indication of the better robustness of ANN as compared to other models. In comparison to the soft techniques investigated in the current study, the model used by [PERSON] et al. Study [7] does not perform well, although it is most likely better than other analytical models for the WAF of unconventional oils [4,6,22]. This observation was confirmed for the training as well as the test datasets. The test dataset was not used for the development of models in this study hence comparison of their accuracy is deemed fair with the analytical model that was developed using a different dataset. This finding justifies the need to employ AI-based models for designing WAF pipeline systems. The analytical model seems to have inadequate generalization capability, although it was developed based on an in-depth analysis of the physics. As a result, the application of an analytical model, such as Equation (1) for designing a WAF system results in a high degree of uncertainty that is unfavorable to both the economic and technical feasibility of an engineering project. Figure 6: Comparison of R-square. Figure 7: Comparison of MAPE.