## 3 Simulation Results
### Prediction Based on Dynamic Data
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Method**} & \multicolumn{10}{c}{**Radiopular Relative Error in Testing (\%)**} & \multicolumn{10}{c}{**Training**} \\ \hline  & & \multicolumn{3}{c}{**Time (s)**} & \(\mathbf{r_{yY}}\) & \(\mathbf{r_{yY}^{2}}\) & **MSE** & **RMSE** & **RMSE** (\%) & **MAPE (\%)** & **PI** \\ \hline SVR (polynomial kernel) & 3.1040 & 0.9948 & 0.9896 & 0.0702 & 0.2650 & 12.0035 & 864.6450 & 6.0174 \\ SVR (Gaussian kernel) & 0.2451 & 0.9933 & 0.9866 & 0.0744 & 0.2727 & 12.3531 & 1350.4329 & 6.1973 \\ NN & 0.8467 & 0.9876 & 0.9754 & 0.1164 & 0.3412 & 15.4534 & 2542.8869 & 7.7748 \\ MARS (piecewise-linear) & & & & & & & & & & & & & & & & & & & \\ MARS (piecewise-cubic) & & & & & & & & & & & & & & & & & & & & & \\ \(k\)-NN & & 0.0018 & 0.9955 & 0.9910 & 0.0489 & 0.2211 & 10.0151 & 595.5354 & 5.0188 \\ RF & & & 51.5903 & 0.9960 & 0.9920 & **0.0376** & 0.1938 & 8.7776 & 15.9674 & **4.3976** \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Performance of machine learning in prediction of carbon based on dynamic data. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Method**} & \multicolumn{10}{c}{**Radiopular Relative Error in Testing (\%)**} & \multicolumn{10}{c}{**Training**} \\ \hline  & & **Time (s)** & \(\mathbf{r_{yY}}\) & \(\mathbf{r_{yY}^{2}}\) & **MSE** & **RMSE** & **RMSE** (\%) & **MAPE (\%)** & **PI** \\ \hline SVR (polynomial kernel) & 3.1040 & 0.9948 & 0.9896 & 0.0702 & 0.2650 & 12.0035 & 864.6450 & 6.0174 \\ SVR (Gaussian kernel) & 0.2451 & 0.9933 & 0.9866 & 0.0744 & 0.2727 & 12.3531 & 1350.4329 & 6.1973 \\ NN & 0.8467 & 0.9876 & 0.9754 & 0.1164 & 0.3412 & 15.4534 & 2542.8869 & 7.7748 & 0.1164 & 0.3412 & 15.4534 & 2542.8869 & 7.7748 \\ MARS (piecewise-linear) & & & & & & & & & & & & & & & & & & & & \\ MARS (piecewise-cubic) & & & & & & & & & & & & & & & & & & & & & & & & \\ \(k\)-NN & & 0.0018 & 0.9955 & 0.9910 & 0.0489 & 0.2211 & 10.0151 & 595.5354 & 5.0188 \\ RF & & & 51.5903 & 0.9960 & 0.9920 & **0.0376** & 0.1938 & 8.7776 & 15.9674 & **4.3976** \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Absolute and relative errors in carbon prediction based on dynamic data. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Method**} & \multicolumn{10}{c}{**Radiopular Relative Error in Testing (\%)**} & \multicolumn{10}{c}{**Average**} \\ \hline  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & \\ \hline SVR (polynomial kernel) & 116.00 & 20.32 & 250.4 & 102.28 & 386.52 & 142.0 & 134.66 & 284.12 & 97.67 & 230.35 & 109.84 & 0.0641 & 0.0205 & 0.0205 & 0.0205 & 0.0264 & 0.1699 & 0.0509 & 0.0660 & 0.1193 & 0.0420 & 0.1489 & 0.0032 \\ SVR (Gaussian kernel) & 105.08 & 10.080 & 227.5 & 29.09 & 182.17 & 1.31 & 254.31 & 225.06 & 139.82 & 250.4 & 137.87 & 0.1150 & 0.0677 & 0.0250 & 0.0270 & 1.1860 & 0.0408 & 0.0033 & 0.0122 & 0.0547 & 0.0644 & 1.2563 & 0.5706 \\ NNN & 30.018 & 34.90 & 174.21 & 26.09 & 135.97 & 137.25 & 31.72 & 317.62 & 53.13 & 226.63 & 57.84 & 0.1440 & 0.253 & 0.1196 & 0.0140 & 0.0066 & 0.4415 & 0.2327 & 0.0646 & 0.3236 \\ MARS (piecewise-cubic) & & & & & & & & & & & & & & & & & & & & & & & & & & & \\ \(k\)-NN & & 30.18 & 36.92 & 16.11 & 12.86 & 15.951 & 1.50 & 1.43 & 14.29 & 3.26 & 3.44 & **15.26** & 0.0172 & 0.0148 & 0.025 & 0.0070 & 0.0003 & 0.0003 & 0.0003 & 0.0003 & 0.0003 & 0.0003 & 0.0003 & 0.0003 & 0.0003 & 0.0003 & 0.0003 & 0.0004 & 0.0014 & 0.0015 & 0.0004 \\ RF & & 16.29 & 34.94 & 352.63 & 335.99 & 384.71 & 69.68 & 115.39 & 37.07 & 34.29 & 465.79 & 30.76 & 0.0039 & 0.172 & 0.129 & 0.1175 & 0.1405 & 0.1774 & 0.0663 & 1.335 & 0.1523 & 0.1917 & 0.1327 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Absolute and relative errors in temperature prediction based on dynamic data. \[