## 3 Experimental Methods and Results
### Experimental Method and Results of the Neuro-Fuzzy Transformation with and without MEP  
The aim of the experiments in this section was to investigate whether adding FMF with MEP to the process and using those new informative features can improve prediction accuracy. The dataset of PM\({}_{10}\) from the four stations was selected for this experiment. The 6 h prior dataset was built on the new features of NFT-MEP. Moreover, the structure from \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Stations**} & \multirow{2}{*}{
\begin{tabular}{c} **Number of** \\ **Hours Prior** \\ \end{tabular} } & \multirow{2}{*}{**Accuracy**} & \multicolumn{4}{c}{**F-Score**} \\ \cline{5-8}  & & & **Class 1** & **Class 2** & **Class 3** & **Average** \\ \hline \multirow{5}{*}{CHM-Yup} & 1 & 75.68\% & 0.7818 & 0.3728 & 0.8519 & 0.6689 \\  & 6 & 76.51\% & 0.7782 & 0.5068 & 0.8733 & 0.7194 \\  & 12 & 76.24\% & 0.7603 & 0.5152 & 0.8738 & 0.7165 \\  & 18 & 76.09\% & 0.7570 & 0.5127 & 0.8726 & 0.7141 \\  & 24 & 75.75\% & 0.7639 & 0.5041 & 0.8694 & 0.7125 \\ \hline \multirow{5}{*}{NAN-Hos} & 1 & 66.06\% & 0.8156 & 0.1738 & 0.5570 & 0.5155 \\  & 6 & 72.59\% & 0.8619 & 0.2561 & 0.6356 & 0.5846 \\ \cline{1-1}  & 12 & 72.05\% & 0.8468 & 0.2823 & 0.6656 & 0.5982 \\ \cline{1-1}  & 18 & 71.24\% & 0.8360 & 0.2472 & 0.6370 & 0.5734 \\ \cline{1-1}  & 24 & 64.04\% & 0.7861 & 0.3385 & 0.4664 & 0.5303 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Model performance of the transformed dataset with different amounts of historical data to predict hourly PM\({}_{2.5}\) with the three classes of output data. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Stations**} & \multirow{2}{*}{
\begin{tabular}{c} **Number of** \\ **Hours Prior** \\ \end{tabular} } & \multirow{2}{*}{**Accuracy**} & \multicolumn{4}{c}{**F-Score**} \\ \cline{5-8}  & & & **Class 1** & **Class 2** & **Class 3** & **Average** \\ \hline \multirow{5}{*}{CHM-Yup} & 1 & 68.59\% & 0.7859 & 0.5457 & 0.3971 & 0.5762 \\  & 6 & 70.90\% & 0.7933 & 0.5810 & 0.4553 & 0.6099 \\  & 12 & 68.60\% & 0.7749 & 0.5554 & 0.4168 & 0.5824 \\  & 18 & 69.93\% & 0.7881 & 0.5688 & 0.4087 & 0.5885 \\  & 24 & 67.49\% & 0.7558 & 0.5571 & 0.4262 & 0.5797 \\ \hline \multirow{5}{*}{NAN-Hos} & 1 & 80.19\% & 0.8888 & 0.4536 & 0.1657 & 0.5027 \\  & 6 & 83.86\% & 0.9092 & 0.5164 & 0.2613 & 0.5623 \\  & 12 & 83.02\% & 0.9037 & 0.5224 & 0.1497 & 0.5253 \\  & 18 & 81.82\% & 0.8957 & 0.5264 & 0.1344 & 0.5188 \\  & 24 & 82.15\% & 0.8856 & 0.5650 & 0.1652 & 0.5386 \\ \hline \multirow{5}{*}{LPA-Met} & 1 & 80.09\% & 0.8765 & 0.6562 & 0.7063 & 0.7463 \\  & 6 & 81.70\% & 0.8748 & 0.7180 & 0.7604 & 0.7844 \\ \cline{1-1}  & 12 & 78.47\% & 0.8690 & 0.7129 & 0.7702 & 0.7840 \\ \cline{1-1}  & 18 & 81.67\% & 0.8742 & 0.7175 & 0.7683 & 0.7867 \\ \cline{1-1}  & 24 & 80.65\% & 0.8648 & 0.7011 & 0.7614 & 0.7758 \\ \hline \multirow{5}{*}{PHY-Kno} & 1 & 81.29\% & 0.9138 & 0.5405 & 0.5428 & 0.6657 \\ \cline{1-1}  & 6 & 82.25\% & 0.8908 & 0.5973 & 0.6692 & 0.7191 \\ \cline{1-1}  & 12 & 81.41\% & 0.8919 & 0.5729 & 0.5968 & 0.6872 \\ \cline{1-1}  & 18 & 77.93\% & 0.8692 & 0.5288 & 0.6050 & 0.6677 \\ \cline{1-1}  & 24 & - & - & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Model performance of the transformed dataset with different amounts of historical data to predict hourly PM\({}_{10}\) with the three classes of output data. Section 2.2, which excludes FMF with MEP as the neural network transformation (NT), was used in the experiment. The comparison results of the NT model and the NFT-MEP model to predict hourly PM\({}_{10}\) with three classes of output data are reported in Table 5, where all results were the averaged value between the two testing datasets. The results in Table 5 revealed that the NFT-MEP model had higher statistical indicators than the NT model in every station, which indicates that the neuro-fuzzy transformation gives better results than the one that is not used.