## 5 Results and Discussions
### Comparative Analysis of Shear Wave Velocity for ML Algorithms  
A comparison of the predicted shear wave velocity profiles from all ML algorithms with the shear wave velocity profiles from conventional SASW inversion is illustrated in Figures 13-17. Each figure shows test 1 to test 10, respectively. Within the depth of 0.5 m, the \(V_{S}\) obtained from conventional SASW is 206.7 m \(\rm s^{-1}\), whereas the \(V_{S}\) values obtained from MLP, RF, SVR, and LR are 213.01, 226.65, 213.25, and 213.74 \(\rm m\ s^{-1}\), respectively, for test 1. Similarly, for the second layer, the \(V_{S}\) values obtained are 240.73, 227.89, 211., 234.84, and 224.18 \(\rm m\ s^{-1}\) from SASW, MLP, RF, SVR, and LR, respectively. The \(V_{S}\) deviated at 4-5 m from the SASW value in test 1, as shown in Figure 13a. Figure 13b shows high similarity between SASW and SVR. However, a significant difference can be noticed over the depth interval of 1-2 m (up to 100 \(\rm ms^{-1}\)). Figure 12: Histograms of the shear wave velocity for each layer. Figure 16: \(V_{\rm S}\) profiles from SASW inversion and ML algorithms for (**a**) test 7 and (**b**) test 8. Figure 17: \(V_{\rm S}\) profiles from SASW and ML algorithms for (**a**) test 9 and (**b**) test 10. Figure 15: \(V_{\rm S}\) profiles from SASW inversion and ML algorithms for (**a**) test 5 and (**b**) test 6. It is apparent from these figures that SVR has outstanding performance in comparison to the other algorithms in predicting shear wave velocities. A few drifts have occurred because there were insufficient data points to train in those layers. A comparison of the experimental data and the theoretical dispersion curve corresponding to the SASW profile is provided in the appendices in order to show what impacts this difference has on the effective theoretical dispersion curves that correspond to the shear wave velocity profiles from SASW and ML, and, subsequently, on how well they compare to the experimental data. The confidence limit, percentage error, RMSE and R\({}^{2}\) were measured for every layer (0-6 m depth) so as to produce a better evaluation of the ML algorithms. Table 4 shows the confidence limit and the percentage error associated with the RMSE of composite values for the 10 test cases. It is visible that after 3 m depth, the confidence limit is lower than 80% in MLP, which is not reliable for design purposes [45]. The limit is found to be greater than 85% for SVR from 0-6 m depth. A detailed guideline on target confidence levels was provided by [PERSON] and [PERSON] [46]. Among all of the algorithms, SVR shows a better prediction, as the confidence limit is greater than 80%. Similarly, RF performs better, except for the last layer of depth. The highest percentage error is less than 15% for SVR, whereas it is greater than 20% for all algorithms. The RMSE of composite values for the 10 test cases for different ML algorithms with conventional SASW inversion is shown in Figure 18. In the SVR algorithm the range of RMSE varies from 6.79 to 34.44 ms\({}^{-1}\) up to 3 m, whereas in MLP it differs from 9.05 to 36.84 ms\({}^{-1}\), in RF from 10.13 to 29.29 ms\({}^{-1}\), and in LR from 10.66 to 31.20 ms\({}^{-1}\). After 3 m, the RMSE nonlinearly increases with depth. For SVR, MLP, RF, and LR, the range of RMSE varies from 46.03 to 51.15 ms\({}^{-1}\), 76.96 to 135.56 ms\({}^{-1}\), 39.98 to 81.51 ms\({}^{-1}\), and 49.28 to 87.95 ms\({}^{-1}\), respectively. The resolution of shear wave velocity is not considerably good after the depth of 3 m. The distribution of datasets (refer to Figure 12) after 3 m of depth may cause a sudden increase of RMSE in the ML model. R\({}^{2}\) assesses how strong the linear relationship is between the conventional SASW and ML algorithms. Figure 19 represents the R\({}^{2}\) of composite values for the 10 test cases. The R\({}^{2}\) of RF, MLP, SVR, and LR is 0.98, 0.95, 0.97, and 0.95, respectively, for the 0-0.5 m layer, which is the highest value of R\({}^{2}\).