## 4 Results and Discussion
### AQI Prediction Model
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Without Imputation**} & \multicolumn{3}{c}{**With Imputation**} \\ \cline{2-7}  & **RMSE** & **MAE** & \(R^{2}\) & **RMSE** & **MAE** & \(R^{2}\) \\ \hline SVM-Polynomial & 26.225 & 20.082 & 0.585 & 25.818 & 19.919 & 0.598 \\ SVM-RBF & 25.548 & 19.422 & 0.606 & 25.730 & 19.597 & 0.600 \\ SVM-Linear & 31.189 & 23.412 & 0.413 & 25.628 & 19.623 & 0.603 \\ Random Forest & 18.435 & 13.711 & 0.795 & 18.423 & 13.707 & 0.795 \\ AdaBoost-Square & 17.877 & 12.747 & 0.807 & 17.871 & 12.734 & 0.807 \\ AdaBoost-Linear & 17.825 & 12.732 & 0.808 & 17.810 & 12.718 & 0.809 \\ AdaBoost-Exponential & 17.822 & 12.733 & 0.808 & 17.815 & 12.729 & 0.808 \\ ANN & 20.451 & 15.329 & 0.748 & 20.312 & 15.213 & 0.751 \\ Stacking Ensemble & 17.801 & 12.855 & 0.809 & 17.792 & 12.856 & 0.809 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Results of ML Algorithms for Changhua F8-AQI Prediction. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Without Imputation**} & \multicolumn{3}{c}{**With Imputation**} \\ \cline{2-7}  & **RMSE** & **MAE** & \(R^{2}\) & **RMSE** & **MAE** & \(R^{2}\) \\ \hline SVM-Polynomial & 40.662 & 31.689 & 0.006 & 41.834 & 32.727 & 0.052 \\ SVM-RBF & 34.879 & 26.977 & 0.269 & 34.852 & 26.948 & 0.270 \\ SVM-Linear & 37.451 & 29.047 & 0.157 & 37.092 & 28.703 & 0.173 \\ Random Forest & 26.765 & 20.281 & 0.570 & 26.786 & 20.299 & 0.569 \\ AdaBoost-Square & 26.282 & 18.781 & 0.585 & 26.288 & 18.799 & 0.585 \\ AdaBoost-Linear & 25.786 & 18.204 & 0.600 & 25.817 & 18.246 & 0.599 \\ AdaBoost-Exponential & 25.747 & 18.144 & 0.602 & 25.773 & 18.185 & 0.601 \\ ANN & 30.919 & 23.753 & 0.426 & 30.803 & 23.647 & 0.430 \\ Stacking Ensemble & 25.630 & 18.255 & 0.605 & 25.655 & 18.294 & 0.604 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Results of ML Algorithms for Changhua F24-AQI Prediction. comparing with the results in the other two cities, Zhongli and Changhua, Fengshan shows the best performance in all evaluation measures. In terms of eight-hour prediction, imputation has a significant impact on SVM-Linear, increasing \(R^{2}\) from 0.318 to 0.546 (as shown in Table 11). RMSE and MAE are also improved by 10% and shift closer to the performance of other SVM kernels. Of the three locations, application of machine learning algorithms has the biggest impact on 8-h predictions in Fengshan, with stacking ensemble providing the greatest improvement, followed by AdaBoost, random forest, ANN, and SVM. This sequence is consistent for all results. As summarized in Table 12, for the 24-h predictions in Fengshan, while overall SVM results are not promising, the other methods show quite acceptable evaluation scores. The top three methods (stacking ensemble, AdaBoost, and random forest) obtained \(R^{2}\) scores exceeding 0.71 for which the MAE and RMSE results are comparable to the F8-AQI prediction for Fengshan. Surprisingly, the stacking ensemble is found to be affected by imputation but, even with imputation, the MAE value is still higher than that of all AdaBoost versions (linear, square, and exponential). AdaBoost and stacking ensemble show consistent results, and AdaBoost generally obtains worse RMSE and \(R^{2}\) but better MAE.