## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Regression Model**} & \multirow{2}{*}{**Output**} & \multicolumn{8}{c}{**Turbulence Class**} \\ \cline{3-10}  & & \multicolumn{2}{c}{**Class A**} & \multicolumn{4}{c}{**Class B**} & \multicolumn{4}{c}{**Class C**} \\  & & \(\mathbf{R^{2}}\) & **RMSE** & **TT (s)** & \(\mathbf{R^{2}}\) & **RMSE** & **TT (s)** & \(\mathbf{R^{2}}\) & **RMSE** & **TT (s)** \\ \hline \multirow{4}{*}{Linear} & _FW Def_ & 0.582 & 0.752 & 0.015 & 0.595 & 0.701 & 0.000 & 0.607 & 0.646 & 0.000 \\  & _EW Def_ & 0.492 & 0.299 & 0.000 & 0.510 & 0.289 & 0.015 & 0.526 & 0.278 & 0.015 \\  & _FW F_ & 0.426 & 37.60 & 0.015 & 0.451 & 34.424 & 0.015 & 0.484 & 30.818 & 0.000 \\  & _EW F_ & 0.600 & 79.123 & 0.015 & 0.597 & 79.278 & 0.000 & 0.604 & 78.690 & 0.000 \\ \hline \multirow{4}{*}{Lasso} & _FW Def_ & 0.576 & 0.757 & 0.000 & 0.587 & 0.708 & 0.031 & 0.598 & 0.654 & 0.000 \\  & _EW Def_ & 0.491 & 0.300 & 0.031 & 0.509 & 0.289 & 0.015 & 0.522 & 0.279 & 0.000 \\  & _FW F_ & 0.426 & 37.611 & 0.015 & 0.451 & 34.427 & 0.046 & 0.484 & 30.827 & 0.109 \\  & _EW F_ & 0.600 & 79.118 & 0.000 & 0.596 & 79.330 & 0.015 & 0.603 & 78.734 & 0.031 \\ \hline \multirow{4}{*}{Ridge} & _FW Def_ & 0.582 & 0.752 & 0.046 & 0.594 & 0.701 & 0.000 & 0.607 & 0.646 & 0.000 \\  & _EW Def_ & 0.492 & 0.299 & 0.031 & 0.510 & 0.289 & 0.046 & 0.525 & 0.279 & 0.015 \\  & _FW F_ & 0.426 & 37.608 & 0.0468 & 0.451 & 34.427 & 0.000 & 0.484 & 30.827 & 0.015 \\  & _EW F_ & 0.600 & 79.116 & 0.015 & 0.596 & 79.332 & 0.015 & 0.603 & 78.734 & 0.000 \\ \hline \multirow{4}{*}{Elastic Net} & _FW Def_ & 0.577 & 0.756 & 0.031 & 0.588 & 0.707 & 0.015 & 0.599 & 0.653 & 0.046 \\  & _EW Def_ & 0.491 & 0.300 & 0.015 & 0.509 & 0.289 & 0.000 & 0.522 & 0.279 & 0.000 \\  & _FW F_ & 0.424 & 37.659 & 0.000 & 0.449 & 34.490 & 0.015 & 0.482 & 30.878 & 0.062 \\  & _EW F_ & 0.600 & 79.121 & 0.000 & 0.596 & 79.330 & 0.031 & 0.603 & 78.735 & 0.000 \\ \hline \multirow{4}{*}{Non-linear} & _FW Def_ & 0.730 & 0.604 & 0.218 & 0.748 & 0.553 & 0.171 & 0.770 & 0.494 & 0.156 \\  & _EW Def_ & 0.564 & 0.277 & 0.218 & 0.576 & 0.269 & 0.187 & 0.585 & 0.260 & 0.109 \\  & _FW F_ & 0.574 & 32.383 & 0.265 & 0.590 & 29.743 & 0.156 & 0.626 & 26.250 & 0.296 \\  & _EW F_ & 0.604 & 78.683 & 0.046 & 0.600 & 78.994 & 0.062 & 0.608 & 78.218 & 0.140 \\ \hline \multirow{4}{*}{Neural Network} & _FW Def_ & 0.682 & 0.655 & 3.843 & 0.773 & 0.525 & 1.312 & 0.805 & 0.455 & 5.000 \\  & _EW Dcf_ & 0.881 & 0.144 & 3.890 & 0.875 & 0.145 & 5.250 & 0.905 & 0.124 & 3.296 \\  & _FW F_ & 0.839 & 19.920 & 648.234 & 0.851 & 17.940 & 772.734 & 0.813 & 18.533 & 408.078 \\  & _EW F_ & 0.996 & 7.570 & 161.015 & 0.996 & 7.031 & 210.843 & 0.997 & 5.778 & 194.812 \\ \hline \multirow{4}{*}{Decision Tree} & _FW Def_ & 0.967 & 0.208 & 0.296 & 0.968 & 0.196 & 0.359 & 0.966 & 0.188 & 0.125 \\  & _EW Def_ & 0.977 & 0.063 & 0.375 & 0.979 & 0.058 & 0.406 & 0.982 & 0.052 & 0.093 \\  & _FW F_ & 0.948 & 11.229 & 0.281 & 0.949 & 10.457 & 0.375 & 0.943 & 10.237 & 0.156 \\  & _EW F_ & 0.997 & 5.912 & 0.265 & 0.998 & 5.421 & 0.125 & 0.998 & 4.805 & 0.171 \\ \hline \multirow{4}{*}{Random Forest} & _FW Def_ & 0.984 & 0.144 & 19.562 & 0.983 & 0.139 & 16.343 & 0.984 & 0.130 & 4.406 \\  & _EW Def_ & 0.986 & 0.049 & 18.656 & 0.988 & 0.045 & 16.578 & 0.989 & 0.042 & 4.343 \\  & _FW F_ & 0.975 & 7.817 & 17.562 & 0.974 & 7.487 & 5.750 & 0.970 & 7.409 & 5.062 \\  & _EW F_ & 0.998 & 4.890 & 18.750 & 0.998 & 4.440 & 4.125 & 0.999 & 3.952 & 21.703 \\ \hline \multirow{4}{*}{Adaptive Boosting} & _FW Def_ & 0.719 & 0.616 & 4.109 & 0.772 & 0.525 & 3.390 & 0.779 & 0.484 & 0.828 \\  & _EW Def_ & 0.912 & 0.124 & 3.859 & 0.924 & 0.113 & 3.046 & 0.940 & 0.098 & 1.453 \\ \cline{1-1}  & _FW F_ & 0.667 & 28.615 & 3.921 & 0.685 & 26.057 & 3.250 & 0.701 & 23.477 & 1.125 \\ \cline{1-1}  & _EW F_ & 0.976 & 19.351 & 3.078 & 0.974 & 19.922 & 1.109 & 0.970 & 21.605 & 1.234 \\ \hline \multirow{4}{*}{Gradient Boosting} & _FW Def_ & 0.808 & 0.509 & 7.734 & 0.825 & 0.461 & 6.750 & 0.845 & 0.405 & 3.078 \\ \cline{1-1}  & _EW Def_ & 0.934 & 0.107 & 7.421 & 0 The random forest model is the most stable in terms of prediction accuracy.