## 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 & 33.639 & 24.799 & 0.098 & 34.194 & 25.034 & 0.068 \\ SVM-RBF & 30.635 & 23.340 & 0.252 & 30.335 & 23.053 & 0.267 \\ SVM-Linear & 37.001 & 28.904 & 0.091 & 36.835 & 28.595 & 0.081 \\ Random Forest & 24.974 & 18.648 & 0.503 & 25.007 & 18.667 & 0.502 \\ AdaBoost-Square & 24.219 & 16.724 & 0.533 & 24.226 & 16.753 & 0.532 \\ AdaBoost-Linear & 24.039 & 16.586 & 0.540 & 24.074 & 16.614 & 0.538 \\ AdaBoost-Exponential & 24.053 & 16.574 & 0.539 & 24.099 & 16.620 & 0.537 \\ ANN & 29.150 & 21.957 & 0.323 & 29.113 & 21.927 & 0.325 \\ Stacking Ensemble & 23.825 & 16.667 & 0.548 & 23.831 & 16.693 & 0.548 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of ML Algorithms for Zhongli F24-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 & 24.308 & 17.981 & 0.526 & 23.244 & 17.135 & 0.567 \\ SVM-RBF & 23.375 & 17.283 & 0.562 & 23.358 & 17.278 & 0.563 \\ SVM-Linear & 24.262 & 18.327 & 0.528 & 26.674 & 20.174 & 0.430 \\ Random Forest & 17.471 & 12.408 & 0.755 & 17.477 & 12.413 & 0.755 \\ AdaBoost-Square & 17.386 & 11.801 & 0.758 & 17.352 & 11.788 & 0.759 \\ AdaBoost-Linear & 17.273 & 11.693 & 0.761 & 17.221 & 11.679 & 0.762 \\ AdaBoost-Exponential & 17.283 & 11.691 & 0.761 & 17.284 & 11.685 & 0.761 \\ ANN & 18.786 & 13.502 & 0.717 & 18.759 & 13.486 & 0.718 \\ Stacking Ensemble & 17.167 & 11.804 & 0.764 & 17.178 & 11.799 & 0.764 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of ML Algorithms for Zhongli F8-AQI Prediction. worst performance for the imputed dataset and an MAE value exceeding 30, and an RMSE value exceeding 40. The best performance is still obtained by the stacking ensemble method, with an \(R^{2}\) score of 0.605, and MAE and RMSE values respectively below 19 and 26. Among all kernels used by SVM, the radial basis function appears to be the most effective for 24-h AQI predictions. Moreover, AdaBoost-exponential slightly underperforms stacking ensemble in terms of \(R^{2}\) and RMSE, but consistently provides better MAE results. Table 10 summarizes the results for the one-hour prediction model without and with \(k\)-NN imputation step in the Fengshan dataset. Stacking ensemble learning outperforms other techniques in terms of RMSE and \(R^{2}\), while SVM obtains the worst performance in every prediction case. However, imputation slightly enhances the results, particularly for the RBF and linear kernels, but not for the polynomial kernel which shows a performance decline using the imputed dataset. Also note that while \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 & 12.419 & 11.09 & 0.907 & 14.116 & 12.683 & 0.880 \\ SVM-RBF & 9.672 & 4.639 & 0.944 & 9.596 & 4.497 & 0.944 \\ SVM-Linear & 9.169 & 7.055 & 0.949 & 10.033 & 7.638 & 0.939 \\ Random Forest & 3.059 & 2.055 & 0.994 & 3.105 & 2.066 & 0.994 \\ AdaBoost-Square & 3.093 & 2.046 & 0.994 & 3.126 & 2.054 & 0.994 \\ AdaBoost-Linear & 3.089 & 2.043 & 0.994 & 3.115 & 2.048 & 0.994 \\ AdaBoost-Exponential & 3.093 & 2.046 & 0.994 & 3.126 & 2.054 & 0.994 \\ ANN & 3.914 & 2.505 & 0.991 & 3.870 & 2.541 & 0.991 \\ Stacking Ensemble & 3.039 & 2.043 & 0.994 & 3.076 & 2.057 & 0.994 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of ML Algorithms for Changhua F1-AQI Prediction.