## 4 Model Prediction Results and Analysis  
### Prediction Results  
The study applied four models to the entire site as well as to each classified zone. The evaluation metrics for the regression models were R\({}^{2}\) and RMSE, where an R\({}^{2}\) value closer to 1 and an RMSE closer to 0 indicate superior model performance. The ML models were optimized through hyperparameter tuning to build the most effective models. Table 3 presents the prediction results of the models for the entire site and for zones classified based on the geotechnical and hydrogeological characteristics. When predicting the compression index for the entire site, the models generally yielded R\({}^{2}\) and RMSE values of approximately 0.67 and 0.08, respectively. Among these, the MLR model and RR model (when the hyperparameter alpha was set to 0.1) exhibited the highest R\({}^{2}\) of 0.6742. For zone 1, located in the northern region, the average R\({}^{2}\) was approximately 0.73, and the RMSE was 0.06. Compared with the entire site, the prediction accuracy improved by about 9% based on the R\({}^{2}\) value, with minimal performance differences among the models. The highest-performing model was the LR model when alpha was set to 0.001. For zone 2, located in the western region, the R\({}^{2}\) values ranged from 0.4621 to 0.5539, and the RMSE values ranged from 0.0345 to 0.0379, indicating the lowest predictive accuracy among the entire site and all classified zones. This is likely due to the zone being influenced by two water systems while also being located the furthest from both, resulting in a low correlation between the input data and the compression index. Among the models, the MLR model demonstrated the best prediction performance. As a result, the accuracy of \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Zone**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Train Data**} & \multicolumn{2}{c}{**Test Data**} \\ \cline{3-6}  & & & \(\mathbf{R^{2}}\) & **RMSE** & **R\({}^{2}\)** & **RMSE** \\ \hline \multirow{4}{*}{Total} & \multirow{2}{*}{Total} & MLR & 0.6258 & 0.0754 & 0.6742 & 0.0794 \\ \cline{3-6}  & & RR & 0.6258 & 0.0754 & 0.6742 & 0.0794 \\ \cline{3-6}  & & LR & 0.6233 & 0.0756 & 0.6699 & 0.08 \\ \cline{3-6}  & & ENR & 0.6258 & 0.0754 & 0.6741 & 0.0794 \\ \hline \multirow{4}{*}{Zone 1} & \multirow{2}{*}{Zone 2} & MLR & 0.5636 & 0.0942 & 0.7294 & 0.0591 \\ \cline{3-6}  & & RR & 0.5621 & 0.0944 & 0.7308 & 0.059 \\ \cline{3-6}  & & ENR & 0.5635 & 0.0942 & 0.7298 & 0.0591 \\ \hline \multirow{4}{*}{Zone 2} & \multirow{2}{*}{Zone 3} & MLR & 0.5002 & 0.0306 & 0.5539 & 0.0345 \\ \cline{3-6}  & & RR & 0.4707 & 0.0315 & 0.5068 & 0.0363 \\ \cline{3-6}  & & LR & 0.4177 & 0.033 & 0.4621 & 0.0379 \\ \cline{3-6}  & & ENR & 0.4181 & 0.033 & 0.4628 & 0.0379 \\ \hline \multirow{4}{*}{Zone 4} & \multirow{2}{*}{Zone 4} & MLR & 0.7159 & 0.0511 & 0.7575 & 0.0459 \\ \cline{3-6}  & & RR & 0.4982 & 0.068 & 0.6543 & 0.0548 \\ \cline{3-6}  & & LR & - & - & - & - \\ \cline{3-6}  & & ENR & - & - & - & - \\ \hline \multirow{4}{*}{Zone 5} & \multirow{2}{*}{Zone 6} & MLR & 0.7804 & 0.0492 & 0.831 & 0.0457 \\ \cline{3-6}  & & RR & 0.7765 & 0.0497 & 0.8405 & 0.0444 \\ \cline{1-1} \cline{2-6}  & & LR & 0.7695 & 0.0504 & 0.8546 & 0.0424 \\ \cline{1-1} \cline{2-6}  & & ENR & 0.7692 & 0.0505 & 0.8527 & 0.0427 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance evaluation of the C\({}_{\text{c}}\) prediction models. the zone 2 model was found to be lower than that of the total model. Such outcomes can arise when the dataset size is not sufficiently large, potentially leading to reduced model accuracy and safety, and consequently causing various issues. For zone 3, located in the eastern region, the R\({}^{2}\) values ranged from 0.6543 to 0.7575, and the RMSE values ranged from 0.046 to 0.055, showing an R\({}^{2}\) difference of up to 0.1 among the models.