## 4 Results and Discussion
### AQI Prediction Model
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Parameter Design (F1/F8/F24)**} \\ \cline{2-5}  & **Zhongli** & **Changhua** & **Fengshan** \\ \hline \multirow{4}{*}{**Random Forest**} & No. of Trees = 100/200/200 & No. of Trees = 200 & No. of Trees = 200 /100/200 \\  & Min. observation = 6/6/3 & Min. observation = 6/3/3 & Min. observation = 6/3/3 \\  & \# of Trees= 100 & & & \\ \multirow{4}{*}{**AdaBoost**} & \(\alpha\) = 0.8/0.9999/0.9 & \(\alpha\) = 0.8/0.9/0.9999 & \(\alpha\) = 0.8/0.9/0.8 \\  & Linear & C = 3/0.1/0.12, \(\upsilon\) = 0.5 & C = 3/0.12/0.1, \(\upsilon\) = 0.5 & \(\upsilon\) = 0.5/0.5/0.9 \\ \multirow{2}{*}{**SVM**} & \multirow{2}{*}{Polynomial} & C = 3/0.7/0.9, \(\upsilon\) = 0.5/0.2/0.1, & C = 3/0.9/0.9, \(\upsilon\) = 0.5/0.2/0.1, & C = 3/0.9/0.9, \(\upsilon\) = 0.5/0.2/0.9, \\  & \(\gamma\) = auto, \(c\) = 3/5/3, d = 1 & \(\gamma\) = auto, \(c\) = 3, d = 1 & \(\gamma\) = auto, \(c\) = 3, d = 1 \\ \multirow{4}{*}{ RBF} & \multirow{2}{*}{C = 3/1/1, \(\upsilon\) = 0.5, \(\gamma\) = auto} & C = 3/3/1, \(\upsilon\) = 0.5, \(\gamma\) = auto & C = 3/3/1, \(\upsilon\) = 0.5/0.5/0.2, \\  & & & & \(\gamma\) = auto \\ \multirow{4}{*}{**ANN**} & \multirow{4}{*}{Activation function: Identity; Optimizer: L-BFGS-B; No. of Input neurons = 24; No. of Hidden neurons over layers = 50/50/50; No. of Output neurons = 1} \\  & & & \(\alpha\) = 0.0001; Max. of Iterations = 300 \\ \cline{1-1} \cline{2-5}  & \multirow{2}{*}{Regularization: L2 Ridge regression; \(\alpha\) = 0.3} \\ \cline{1-1} \cline{2-5}  & & & & \\ \end{tabular}
\end{table} 
Table 3: Parameter Design of ML Methods. respectively. However, ANN and random forest perform better than SVM, with \(R^{2}\) scores exceeding 0.7 and error metrics just slightly lower than those obtained with AdaBoost and stacking ensemble. The results match the expectation since the uncertainty increases with the longer period and leads to higher difficulty in the forecast. The study also finds that the overall values are worse than that of the F1-AQI prediction. Table 6 shows that no method used for targeting F24-AQI prediction produced an \(R^{2}\) score above 0.6, with the lowest score of 0.091. Simply put, the yielded predictions fit the dataset poorly. Stacking ensemble still ranks first, but the \(R^{2}\) gap to the second-best method (AdaBoost-Linear) is larger than in the previous cases. SVM performance tracked far behind the other methods with the highest score for evaluation metrics obtained by RBF kernel. However, the \(R^{2}\) score is so low that the SVM method is considered not preferable for 24-h prediction. Predictive model results for F1-AQI Changhua are similar to those for F1-AQI Zhongli. Stacking ensemble, AdaBoost, and random forest provide the best performance for one-hour AQI level prediction (see Table 7). These algorithms perform better for all evaluation metrics in Changhua than in Zhongli. Also, the imputation process reduces the performance of SVM, but not the other algorithms. When it comes to the F8-AQI prediction (as shown in Table 8), the Changhua prediction again outperforms that of Zhongli. AdaBoost and stacking ensemble both yield \(R^{2}\) scores exceeding 0.8. Without imputation, stacking ensemble outperforms the other methods. However, with imputation, AdaBoost performance is comparable to that of stacking ensemble. SVM-linear gives the highest MAE and RMSE results, i.e., 23.412 and 31.189, respectively. These error metrics can be further reduced to 19.623 and 25.628 by imputation. In the Zhongli dataset, the time step selection affects the performance of machine learning methods, and this is consistent with the results for the F24-AQI prediction models in Changhua (Table 9). Declination occurs across all models with a very low \(R^{2}\). The SVM-Polynomial gives the