## 3 Experimental Methods and Results  
In this section, various experiments are presented to find the appropriate structure of the proposed model or to confirm model performance. The details of the experimental design consist of four subsections. The first three subsections are experiments to predict the class of PM one hour in advance. The first one found the best time interval for the new informative features. The second one was used to confirm that the new informative features created from FMF with MEP can increase the prediction performance. These experiments used four out of eight stations. The first two data monitoring stations were the CHM-Yup and NAN-Hos stations, due to the availability of the PM\({}_{2.5}\) data. The other two stations were the LPA-Met and PHY-Kno stations. The third subsection implemented the proposed model to all data monitoring stations and the overall model performances were reported. In addition, other popular prediction models in this problem were selected to compare the model performance with the proposed model. The last subsection was the reported model performance of the proposed model to predict an additional period of output data up to three hours in advance. To obtain accurate prediction results, a specific data set for the dry season from 1 February to 31 May of each year, during which air pollution levels in Thailand are high, was the focus of this research. The dataset during the crisis of the last two years was defined as the testing data. The first set was raw data between 1 February 2018, and 30 April 2018. The second set was raw data between 1 February 2017, and 31 May 2017, while the remaining years were selected as the training data. Experimental Method and Results for the New Informative Features with Different Number of Historical Data  
This experiment aimed to determine an appropriate number of hours before the generation of the new informative features, as described in Section 2.2.4. The dataset of the five different time periods, 1, 6, 12, 18, and 24 h, was used in the experiments. Therefore, each dataset had a different number of features that varied from 1 to 12 depending on the number of hours prior. The experiments in this subsection used three classes that were defined per the T-AQI standard, as detailed in Table 2. The hourly PM\({}_{10}\) class prediction was used in four stations, while the hourly PM\({}_{2.5}\) class prediction was used in two stations, due to the reason described earlier. Table 3 shows the results of the class prediction of PM\({}_{10}\) with the F-Score separated by class and the average overall and average accuracy of the two testing datasets. In addition, the PHY-Kno station had no experimental result from 24 h prior due to a lack of continuity data. The results shown in Table 3 in the last column show that the usage of 6 h usage had the highest F-score in three out of the four stations, CHM-Yup, NAN-Hos, and PHY-Kno stations. In the LPA-Met Station, there was no clear F-score result for any time period as with the other stations. In addition, 6 h prior had the highest average accuracy in every station. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{3}{c}{**Three Classes of Output Data**} & \multicolumn{3}{c}{**Five Classes of Output Data**} \\ \hline \multirow{2}{*}{**Class Name**} & \multicolumn{2}{c}{**Hourly Value**} & \multirow{2}{*}{**Class Name**} & \multicolumn{2}{c}{**Hourly Value**} \\ \cline{2-2} \cline{4-6}  & **PM\({}_{10}\)** & & **PM\({}_{2.5}\)** & & **PM\({}_{10}\)** & **PM\({}_{2.5}\)** \\ \hline \multirow{2}{*}{Class 1} & \multirow{2}{*}{0–80} & \multirow{2}{*}{0–37} & Class 1 & 0–50 & 0–25 \\  & & & Class 2 & 51–80 & 26–37 \\ \multirow{2}{*}{Class 2} & \multirow{2}{*}{81–120} & \multirow{2}{*}{38–50} & Class 3 & 81–120 & 38–50 \\  & & & Class 4 & 121–180 & 51–90 \\ \multirow{2}{*}{Class 3} & \multirow{2}{*}{\textgreater{}120} & \multirow{2}{*}{\textgreater{}50} & Class 5 & \textgreater{}180 & \textgreater{}90 \\ \hline \hline \end{tabular}
\end{table}