## 3 Results and Discussions  
### Models  
#### 3.1.1 Site-Specific Models  
In this study, site-specific characteristics were utilized as model inputs. For example, the SPT N-values and groundwater elevation were used to calculate the unit weights of a soil layer. In cases where data were unavailable for specific locations, the study collected site-specific geotechnical data, including fines content, soil type, and ground elevation, to train and develop machine learning models. Regression models are utilized for numerical data, whereas classification models are utilized for non-numerical data. Various performance parameters are compared during model training to determine the optimal deployment model. In this study, it was determined that the Tree regression model provided the best fit for the available data, shown on Tables 2 and 3. The algorithm employed by the tree model repeatedly divides the data into partitions based on latitude, longitude, and elevation, as well as the output variable. With R\({}^{2}\) values ranging from 0.76 to 0.99, the trained models for ground elevation, groundwater table elevation, SPT N-value, and fines content demonstrated a very strong positive relationship. The lowest RMSE values were also observed for the tree model, indicating a good fit with the data. In addition, with an RMSE of 0.00 m for both the ground elevation and the groundwater table elevation, the tree model provided a perfect fit. Utilizing the tree model in geotechnical site-specific models has numerous benefits. It requires less work during data preparation and preprocessing, and data normalization and scaling are unnecessary. In addition, missing values in the data do not have a significant impact on the process of constructing a tree, which is especially advantageous in instances where geotechnical investigation reports may contain insufficient data. In previous sections, the number of usable borehole data was determined by examining the R\({}^{2}\) and RMSE values. A comparison was conducted to determine how the number of collected data impacts the performance of machine learning models that have been trained. As anticipated, the results demonstrated that as the density of the boreholes increased, the RMSE decreased and the R\({}^{2}\) increased, resulting in improved model performance. It is commonly understood that insufficient training data can result in less accurate estimates. Similarly, insufficient testing data can lead to overly optimistic results and a high degree of variation. The winning model's performance parameters, R\({}^{2}\) and RMSE, were enhanced through hyperparameter tuning. Since the winning model is the tree model, the number of leaves was the hyperparameter being tuned. The performance of the tree regression model \begin{table}
\begin{tabular}{l c c c c c} \hline
**Parameter** & **Tree** & **Linear** & **Quadratic** & **Ensemble** & **Neural Network** \\ \hline Ground Elevation & 0.99 & 0.44 & 0.71 & 0.98 & 0.93 \\ \hline Groundwater Table Elevation & 0.99 & 0.44 & 0.70 & 0.97 & 0.92 \\ \hline SPT N-value & 0.88 & 0.09 & 0.25 & 0.50 & 0.46 \\ \hline Fines Content & 0.76 & 0.05 & 0.16 & 0.48 & 0.30 \\ \hline \end{tabular}
\end{table} 
Table 2: Coefficient of determination (R\({}^{2}\)) of the site-specific models. \begin{table}
\begin{tabular}{l c c c c c} \hline
**Parameter** & **Tree** & **Linear** & **Quadratic** & **Ensemble** & **Neural Network** \\ \hline Ground Elevation & 0.00 & 2.81 & 2.50 & 1.24 & 1.80 \\ \hline Groundwater Table Elevation & 0.00 & 2.95 & 2.62 & 1.30 & 1.89 \\ \hline SPT N-value & 7.33 & 20.24 & 18.41 & 15.09 & 15.73 \\ \hline Fines Content & 13.78 & 27.60 & 25.97 & 20.38 & 23.75 \\ \hline \end{tabular}
\end{table} 
Table 3: Root mean square error (RMSE) of the site-specific regression models. declined as the number of leaves increased, thus, \"2\" leaves were utilized. This is because a small change in the model can result in a substantial change in the tree's structure, leading to greater variability.