## 5 Results and Discussion
### Comparison of ML Models' Performance  
To compare the efficiency of the ML models, different performance metrics such as overall accuracy, sensitivity, precision, and F1_score are utilized. The results of this evaluation are summarized in Tables 11 and 12. Table 11 shows that the SVM model achieved the highest overall accuracy of 99.84%.  
The ANN, RF, and DT models also performed well, achieving overall accuracies of 98.82%, 99.23%, and 95.67%, respectively. It is important to note that the datasets were imbalanced, and therefore, it is necessary to consider both the overall accuracy and other performance metrics for each soil type to accurately assess the ML models' performance.  
Table 12 presents the performance metrics of the ML models for each soil type. The table shows the sensitivity, precision, and F1_score values of each model and soil type. These metrics indicate the models' efficiency in correctly identifying the soil type. Across all models, the sensitivity, precision, and F1_score values for each soil type are very high, indicating that the models successfully identified instances of all classes. However, the efficiency of the ANN model on minority class 7 was low compared to the other models. It scored lower sensitivity and F1_score values of 0.67 and 0.77, respectively, compared to the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**ML Models**} & \multirow{2}{*}{**Soil Type**} & \multirow{2}{*}{**Sensitivity**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**F1\_Score**} \\ \hline \multirow{8}{*}{ANN} & 2 & 0.96 & 0.99 & 0.98 \\  & 3 & 0.96 & 0.97 & 0.96 \\  & 4 & 0.99 & 0.99 & 0.99 \\  & 5 & 0.99 & 0.99 & 0.99 \\  & 6 & 0.98 & 0.97 & 0.98 \\  & 7 & 0.67 & 0.91 & 0.77 \\ \hline \multirow{8}{*}{RF} & 2 & 1 & 0.99 & 0.99 \\  & 3 & 1 & 0.99 & 0.99 \\  & 4 & 1 & 0.99 & 0.99 \\  & 5 & 1 & 0.99 & 0.99 \\  & 6 & 1 & 0.99 & 0.99 \\  & 7 & 0.98 & 0.96 & 0.97 \\ \hline \multirow{8}{*}{SVM} & 2 & 1 & 1 & 1 \\  & 3 & 1 & 1 & 1 \\  & 5 & 1 & 1 & 1 \\  & 6 & 0.99 & 1 & 0.99 \\  & 7 & 0.97 & 0.93 & 0.95 \\ \hline \multirow{8}{*}{DT} & 2 & 0.98 & 0.95 & 0.96 \\  & 3 & 0.93 & 0.96 & 0.95 \\ \cline{1-1}  & 4 & 0.96 & 0.95 & 0.96 \\ \cline{1-1}  & 5 & 0.96 & 0.96 & 0.96 \\ \cline{1-1}  & 6 & 0.93 & 0.94 & 0.94 \\ \cline{1-1}  & 7 & 0.94 & 0.83 & 0.88 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Performance comparisons of ML models on test datasets.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**ML Models**} \\ \cline{2-5}  & **ANN** & **RF** & **DT** & **SVM** \\ \hline Overall Accuracy (\%) & 98.82 & 99.23 & 95.67 & 99.84 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Performance metrics of ML models for each soil type.  
SVM and RF models with almost perfect scores for all metrics. This indicates that the ANN model needs additional data to better identify minority classes.  
The SVM and RF models outperformed the ANN and DT models in terms of sensitivity, precision, and F1_score values for all soil types. These two models achieved almost perfect scores for all performance metrics for all soil types, indicating their high accuracy in the classification task. Overall, the performance of the ML models in classifying soils based on the CPT dataset is consistent with previous similar research carried out on ML techniques (e.g., see [12,21]).