## 5 Results
Selected performance measures are presented for the EoNN members, including \(\mathit{RMSE}\), \(\mathit{MAPE}\), and maximum values of \(\mathit{APE}^{P}\) in Table 8.  
After the selection of EoNN members, computations of the combined predictions of the entire EoNN were performed. As mentioned earlier, two variants of ensembles were considered in the course of the research:  
* Model based on simple averaging--later referred to as \(\mathrm{SAV}_{\mathrm{ENS}}\);
* Model based on generalized averaging--later referred to as \(\mathrm{GAV}_{\mathrm{ENS}}\). Firstly, the weights \(\alpha_{k}\) were computed. For \(\mathrm{SAV}_{\mathrm{ENS}}\), the computations based on Equation (7) were straightforward, resulting in the following:  
\[\alpha_{1}=\alpha_{2}=\alpha_{3}=\alpha_{4}=\alpha_{5}=0.2\] \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**ANN** & **MLP Structure** & \begin{tabular}{c} **Activation** \\ **Function Hidden Layer** \\ \end{tabular} & \
\begin{tabular}{c} **Activation** \\ **Function Output Layer** \\ \end{tabular} &
\begin{tabular}{c} **Number of** \\ **Training Epochs** \\ \end{tabular} \\ \hline ANN\({}_{\mathrm{F1}}\) & 12–9–1 & Exponential & Linear & 22 \\ ANN\({}_{\mathrm{F2}}\) & 12–12–1 & Hyperbolic tangent & Exponential & 34 \\ ANN\({}_{\mathrm{F3}}\) & 12–12–1 & Exponential & Hyperbolic tangent & 15 \\ ANN\({}_{\mathrm{F4}}\) & 12–9–1 & Hyperbolic tangent & Hyperbolic tangent & 42 \\ ANN\({}_{\mathrm{F5}}\) & 12–11–1 & Hyperbolic tangent & Linear & 65 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Characteristics of the ANN selected to be the ensemble members. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**ANN** & \(\mathit{RMSE}_{\mathit{L6V}}\) & \(\mathit{RMSE}_{\mathit{T}}\) & \(\mathit{MAPE}_{\mathit{L6V}}\) & \(\mathit{MAPE}_{\mathit{T}}\) & \begin{tabular}{c} \(\mathit{max}\) \\ \(\mathit{[APE^{P},\mathit{L6V}]}\) \\ \end{tabular} & \
\begin{tabular}{c} \(\mathit{max}\) \\ \(\mathit{[APE^{P},\mathit{L6V}]}\) \\ \end{tabular} &
\begin{tabular}{c} \(\mathit{max}\) \\ \(\mathit{[APE^{P},\mathit{L6V}]}\) \\ \end{tabular} \\ \hline ANN\({}_{\mathrm{F1}}\) & 453 & 594 & 19.6\% & 15.0\% & 92.1\% & 41.5\% \\ ANN\({}_{\mathrm{F2}}\) & 508 & 521 & 19.2\% & 14.7\% & 80.2\% & 58.3\% \\ ANN\({}_{\mathrm{F3}}\) & 533 & 506 & 22.0\% & 16.9\% & 93.9\% & 54.7\% \\ ANN\({}_{\mathrm{F4}}\) & 457 & 391 & 17.6\% & 16.9\% & 67.8\% & 50.9\% \\ ANN\({}_{\mathrm{F5}}\) & 402 & 460 & 15.2\% & 15.9\% & 81.5\% & 27.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Selected performance measures of the ANN selected to be the EoNN members. For GAV\({}_{\text{ENS}}\), computations relied on Equations (8) and (9) and required more effort. The weights for combining outputs provided by the members of the ensemble are presented below. \[\alpha_{1}=0.109;\,\alpha_{2}=0.242;\,\alpha_{3}=-0.185;\,\alpha_{4}=0.170;\, \alpha_{5}=0.664\]  
For both SAV\({}_{\text{ENS}}\) and GAV\({}_{\text{ENS}}\) models, further computations based on the respective weights \(\alpha_{k}\) and Equation (5) were conducted to obtain level-1 predictions of sewerage project construction costs. Figures 3 and 4 present scatter plots that represent real-life values of updated construction costs of sewer projects \(y\) and, on the contrary, values predicted by the developed models \(\hat{y}\). Scatter plots, which are alternatively referred to as scattergrams or scatter charts, serve the purpose of providing a visual representation of data points within a two-dimensional Cartesian coordinate system. Each data point is depicted as a point or dot, facilitating the observation of the relationship between the actual values \(y\) and the predicted values \(\hat{y}\). In essence, each data point on the plot corresponds to a pair of associated values.