## 3 Results
On the other hand, RBFNN calculates the centroid for each node and takes the difference from the input parameters, in which case each node can act as a classifier, where the centroid is calculated to be closer to a specific class of dataset. Therefore, RBFNN becomes more suitable for a hierarchical model. The accuracy of the hierarchical model was found to be less than the models for moisture-damaged and aged samples. This is understandable because the samples for the hierarchical model contain a lot more variation than the other models. However, the accuracy of the hierarchical (RBFNN) model was not totally unacceptable, as the CC value was found to be 0.8 or more and the MAPE values were close to 20%. Considering the convenience offered by using the hierarchical model, these accuracy levels can be considered sufficient, and its use can be recommended. Further improvements can be made in this model with a larger dataset. The performance for ensembles for each case is shown in Tables 7-9, while their scatterplots are shown in Figures 9-11. Their accuracy was compared with individual models with the best accuracy in each case, which were RBFNN for the hierarchical model compared with MLP for moisture-damaged and aged samples. It can be observed that ensemble learning resulted in a small improvement in the accuracy, compared to the respective best ANN model, in all the cases. Figure 8: Scatterplot for RBFNN of aged samples. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Dataset** & **RMSE** & **MAPE** & **CC** \\ \hline \multirow{3}{*}{RBFNN} & Train & 44.68 & 20.07 & 0.84 \\ \cline{2-5}  & Test & 51.49 & 22.64 & 0.79 \\ \hline \multirow{3}{*}{Simple average ensemble} & Train & 42.06 & 21.21 & 0.84 \\ \cline{2-5}  & Test & 42.33 & 21.70 & 0.82 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance measures for ensemble and RBFNN for hierarchical models. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Dataset** & **RMSE** & **MAPE** & **CC** \\ \hline \multirow{3}{*}{MLP} & Train & 35.58 & 13.84 & 0.89 \\ \cline{2-5}  & Test & 46.20 & 17.98 & 0.88 \\ \hline \multirow{3}{*}{Simple average ensemble} & Train & 39.11 & 13.16 & 0.95 \\ \cline{2-5}  & Test & 40.30 & 14.99 & 0.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance measures for ensembles and MLP for models of moisture-damaged samples. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Dataset** & **RMSE** & **MAPE** & **CC** \\ \hline \multirow{3}{*}{MLP} & Train & 31.39 & 15.63 & 0.92 \\ \cline{2-5}  & Test & 31.74 & 22.52 & 0.84 \\ \hline \multirow{3}{*}{Simple average ensemble} & Train & 25.80 & 19.07 & 0.86 \\ \cline{2-5}  & Test & 25.84 & 19.53 & 0.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performance measures for ensembles and MLP for models of aged samples. Figure 9: Scatterplot of hierarchical ensemble. The test accuracies of this study were compared with another similar study carried out by [PERSON] et al. [39]. In doing so, it was found that the accuracies of this study's models on test datasets for aged and moisture-damaged samples were slightly better. CC values obtained in the above study were 0.8 or less while in this study they are well above this value for the respective cases. Moreover, the MAPE for said study went as high as 26% in some cases, while it does not go above 22.52% in this study for aged or moisture-damaged samples. Hierarchical modeling was applied for the first time in this study, hence it could not be compared with any previous study.