## 6 Discussion  
As evident from the literature analysis, attempts to develop cost analysis models for sewerage projects have been made [19; 20; 21; 22; 23; 24; 25; 26]. In comparison to the analyses presented in this article, it can be noted that the selection of cost predictors, in terms of their nature, is similar. However, it should not be forgotten that local construction market conditions and data availability also influence the research. The final set of cost predictors and their values strongly depend on the possibility of obtaining them, which results in some differences between the models presented in the literature. Among the mentioned works, some are based on the use of ANN [25; 26]. Unfortunately, it is challenging to compare the results of these studies with the findings of this research. The cited works used less data for training and testing, and the models are based on single networks. Most importantly, there is a lack of precise information about the types of neural networks used and essential details regarding the training and testing processes of ANN and the analysis of their performance. Thus, it was decided, for the purpose of further assessing research results, to develop a benchmark model based on multiple regression using the classical least square method. The benchmark model is hereinafter referred to as MR. The general formula for predictions based on the MR model is provided below. \[\hat{g}=\hat{\rho}_{0}+\sum_{j}\hat{\rho}_{j}x_{j} \tag{16}\]  
where:  
\(\hat{\rho}_{0}\), \(\hat{\rho}_{j}\)--regression coefficients. To ensure the comparability of the MR benchmark model with the models based on the EoNN approach developed during the research, the computation of coefficients \(\beta_{0}\) and \(\hat{\rho}_{j}\) was performed with the use of subset C, equivalent to the training subset for ANNs that became members of \(\text{SAV}_{\text{ENS}}\) and \(\text{GAV}_{\text{ENS}}\) (including cases used for learning and validation processes). For testing the MR model, subset \(T\) was used. Below are the coefficients obtained from the regression analysis, along with the standard errors of estimation provided in the brackets. \(\beta_{0}=-5588.5\) (1420.85); \(\beta_{1}=1064.6\) (376.12); \(\beta_{2}=-39\),347.1 (62,376.80);  
\(\beta_{3}=1414.0\) (1025.67); \(\beta_{4}=2416.5\) (727.67); \(\beta_{5}=2821.5\) (548.23);  
\(\beta_{6}=30.8\) (235.41); \(\beta_{7}=240.0\) (358.94); \(\beta_{8}=3329.0\) (763.03); \(\beta_{9}=1340.1\) (635.74);  
\(\beta_{10}=38\),154.7 (53,700.36); \(\beta_{11}=18\),397.4 (20,636.69); \(\beta_{12}=36.9\) (449.73)  
Figure 7 displays the results for the MR model in the form of a scatter plot of \(y\) and \(\hat{y}\) values (compare with Figures 3 and 4). Correlation coefficients \(R\) were computed in a similar manner as in the case of the \(\text{SAV}_{\text{ENS}}\) and \(\text{GAV}_{\text{ENS}}\) models;  
* For MR: \(R=0.926\) for the \(C\) subset and \(R=0.940\) for the \(T\) subset;  
When compared to the correlations computed for the developed EoNN-based models, the differences that occur are relatively insignificant. However, an analysis of the scatter plots and a comparison with those presented for EoNN-based models reveal greater dispersion and deviations from the line of perfect fit in the case of the MR benchmark model;An analysis of the \(PE^{p}\) distributions for the MR benchmark model reveals the superiority of EoNN-based models. For MR, the shares of \(PE^{p}\) within the range \(<\)\(-\)30%; 30%\(>\) were as follows:  
* 68.9% for the \(C\) subset and 73.7% for the \(T\) subset;  
Table 10 provides a summary and performance measures for the MR benchmark model (compare with Table 9). Figure 8 depicts the distributions of percentage errors \(PE^{p}\), similar to those presented for the \(\text{SAV}_{\text{ENS}}\) and \(\text{GAVE}_{\text{NS}}\) models (compare Figures 5 and 6).