## 5 Discussion
### The Variable Importance of Input Parameters  
Investigating the significance of the input parameters, is a crucial aspect of artificial intelligence modeling. In this study, the impact of the individual input parameters on network error was examined, by altering each parameter by 100%, while maintaining all other inputs at their actual values. The resulting network errors were recorded for each alteration, and are presented in Figure 11 for each of the three AI models tested, namely artificial neural network (ANN), support vector machines (SVM), and genetic programming (GP). A greater network error resulting from a given parameter alteration, indicates that the network exhibits increased sensitivity to that particular parameter. The variable importance ranking was evaluated based on the input parameters, including liquid limit of soil (LL-Soil), plasticity index of soil (PI-Soil), \(G_{\mathrm{s}}\) of soil, number of compaction blows (Compaction Num. Hammer), optimum moisture content of soil (OMC-Soil), maximum dry density of soil (MDD-Soil), optimum moisture content of mixture (OMC-mixture), maximum dry density of mixture (MDD-Mixture), and sludge content (% Sludge). Table 11 summarizes the ranking of inputs based on the different AI models. The results reveal, that the SVM model ranked the LL of soil as the most important input parameter, followed by the number of compaction blows and the PI of soil. While the MDD of soil was the least important parameter. The ANN ranked the number of compaction blows as the most important input parameter, followed by the PI and LL of soil. While the MDD of the mixture was the least important parameter. The GP ranked the number of compaction blows as the most important input parameter, followed by sludge content and LL of soil. For all three models, and according to Table 11, it can be concluded that the number of compaction blows and LL of soil parameters, are the most important input parameters, while the MDD of soil and mixture, are the least important. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Ranking**} & \multicolumn{8}{c}{**Input Parameters**} \\ \cline{2-9}  & **LL-Soil** & **PI-Soil** & **G\({}_{\mathrm{s}}\)-Soil** &
\begin{tabular}{c} **Compaction** \\ **Num. Hammer** \\ \end{tabular} & **OMC-Soil** & **MDD-Soil** & **OMC-Mixture** & **MDD-Mixture** & **\% Sludge** \\ \hline SVM & 1 & 3 & 4 & 2 & 7 & 9 & 8 & 6 & 5 \\ \hline ANN & 3 & 2 & 5 & 1 & 7 & 8 & 6 & 9 & 4 \\ \hline GP & 3 & 4 & 5 & 1 & 6 & 9 & 7 & 8 & 2 \\ \hline Total & 7 & 9 & 14 & 4 & 20 & 26 & 21 & 23 & 11 \\ \hline Ranking & 2 & 3 & 5 & 1 & 6 & 9 & 7 & 8 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: The importance of different input parameters for all AI models. Figure 11: The importance of input parameters on the MAE of the best (**a**) SVM, (**b**) ANN, and (**c**) GP models. The explanation for the variation in the importance ranking of input parameters across AI models, could be the differences in the underlying algorithms and architectures. SVM is a kernel-based method, that tries to find a hyperplane that maximally separates data points. ANN is a feedforward neural network with hidden layers, and it is trained using a backpropagation algorithm. GP is an evolutionary algorithm, that evolves a population of candidate solutions using genetic operators, such as mutation and crossover. Therefore, these algorithms have different ways of handling the input parameters and constructing models. Another explanation for the variation in importance ranking, could be the nature of the input parameters themselves. Some parameters may have stronger correlations with the target variable, which is CBR, in certain datasets or applications, while others may be less relevant. Moreover, different input parameters may interact with each other in complex ways, making it challenging to determine their individual contributions to the model's performance.