## 5 Results and Discussions
### Comparative Analysis of Shear Wave Velocity for ML Algorithms
The lowest value is found to be 0.72 at 2-2.5 m depth for RF, 0.55 at 2-2.5 m depth for SVR, 0.52 at 2-2.5 m depth for MLP, and 0.55 at 2-2.5 m for LR. The R\({}^{2}\) value of LR also decreases with depth but the value increases marginally at 4-4.5 m, and then begins to decline again. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Confidence Limit (\%)**} & \multicolumn{3}{c}{**Percentage Error (\%)**} \\ \hline
**Depth (m)** & **MLP** & **RF** & **SVR** & **LR** & **MLP** & **RF** & **SVR** & **LR** \\ \hline
0–0.5 & 97.41 & 97.10 & 98.06 & 96.95 & 2.59 & 2.90 & 1.94 & 3.05 \\
0.5–1 & 92.99 & 93.18 & 93.59 & 92.11 & 7.01 & 6.82 & 6.41 & 7.89 \\
1–1.5 & 93.04 & 91.63 & 93.50 & 91.57  
Figure 19: Coefficient of determination for all of the algorithms. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**MLP**} & \multicolumn{3}{c}{**RF**} & \multicolumn{3}{c}{**SVR**} & \multicolumn{3}{c}{**LR**} \\ \hline
**Depth** & \(\mathbf{R^{2}}\) & **MSE** & **RMSE** & \(\mathbf{R^{2}}\) & **MSE** & **RMSE** & \(\mathbf{R^{2}}\) & **MSE** & **RMSE** & \(\mathbf{R^{2}}\) & **MSE** & **RMSE** \\ \hline
0–0.5 & 0.95 & 6.00 & 9.05 & 0.98 & 4.07 & 10.13 & 0.97 & 3.13 & 6.79 & 0.95 & 6.40 & 10.66 \\
0.5–1 & 0.81 & 19.72 & 24.54 & 0.82 & 18.11 & 23.86 & 0.83 & 16.67 & 22.42 & 0.78 & 21.58 & 27.60 \\
1–1.5 & 0.72 & 18.22 & 24.35 & 0.73 & 18.81 & 29.30 & 0.75 & 16.02 & 22.74 & 0.57 & 22.20 & 29.51 \\
1.5–2 & 0.72 & 23.23 & 29.64 & 0.76 & 18.07 & 25.87 & 0.78 & 20.73 & 28.35 & 0.66 & 24.66 & 31.20 \\
2–2.5 & 0.56 & 26.48 & 32.08 & 0.72 & 20.29 & 27.40 & 0.52 & 23.67 & 34.44 & 0.55 & 26.17 & 31.19 \\
2.5–3 & 0.66 & 29.08 & 36.84 & 0.81 & 18.08 & 24.91 & 0.69 & 22.30 & 29.60 & 0.64 & 24.64 & 30.51 \\
3–3.5 & 0.71 & 47.36 & 76.95 & 0.74 & 26.67 & 39.50 & 0.85 & 28.06 & 46.17 & 0.69 & 52.37 & 67.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: MSE, RMSE, and \(\mathbf{R^{2}}\) for ML. Figure 18: RMSE vs. depth for all algorithms. Figure 19: Coefficient of determination for all of the algorithms. In Figure 20a-l a schematic representation of the R\({}^{2}\) of composite values for the 10 test cases between SVR and SASW can be analyzed. In the first layer (0-0.5 m), the R\({}^{2}\) between SVR and SASW is high. The second layer (0.5-1 m) also reveals a strong association. The association between SVR and SASW is fairly high for the third layer (1-1.5 m) and the fourth layer (1.5-2 m). The R\({}^{2}\) value is 0.85, which is a significant value at a depth of 3-3.5 m, including a few outliers, as seen in Figure 20g. The coefficient of determination increases significantly to 0.92 and 0.93 after 4 m, which is known to represent a good association between the shear wave velocity obtained from SVR and SASW, as seen in Figure 20i. At these layers, there are very few data points, and the distribution of the datasets can create a greater RMSE than the other layers. Figure 20: _Cont._  
Figure 20: Cont. Although an R\({}^{2}\) close to 1 is a reasonable match, this value alone does not decide if the data points or predictions are biased. The performance also depends on the outliers and regression line, which is close to the points. It can be concluded that the performance of SVR is better than that of the other algorithms when it comes to predicting the shear wave velocity. The other algorithms have high RMSE after 3 m of depth. Indeed, some outliers can be observed after 3 m of depth. The range of the datasets is broad, but the point that we obtain from SASW is low at this depth, which may affect the ML models.