## 3 Experimental Methods and Results
### Comparison Results between the NFT-MEP Model and Other Popular Models  
To verify the performance of the proposed NFT-MEP model, the other popular models in this problem were selected, including LSTM [15], ARIMA [12], and ARIMAX [34], for comparison. Every other model adjusted the structures to find appropriate parameters. The experimental design in this section differed from the previous section. Four additional stations, namely, CHR-Env, MHS-Env, LPH-Sta, and PHA-Met stations, were selected, so there were eight stations in this experiment. Moreover, the five classes of output data, for which the details are shown in Table 2, were selected to create a prediction model. Finally, each station was applied to four prediction models, namely, NFT-MEP, LSTM, ARIMA, and ARIMAX, and two different output data, including three and five classes. To compare model performance, three statistical indicators, namely, accuracy, F-score, and MCC, were used in this subsection. The comparison results of the four models to predict hourly PM\({}_{10}\) with three and five classes of output data are reported in Table 6. All results were an average value between two testing datasets from all stations. The results for the three classes of output data showed that the NFT-MEP model had the highest average accuracy with a value between \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Stations**} & \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Statistic Indicators**} \\ \cline{3-4}  & & **Accuracy** & **F-Score** \\ \hline \multirow{2}{*}{CHM-Yup} & NT & 70.90\% & 0.6099 \\  & NFT-MEP & 81.99\% & 0.7012 \\ \hline \multirow{2}{*}{NAN-Hos} & NT & 83.86\% & 0.5623 \\  & NFT-MEP & 90.83\% & 0.6253 \\ \hline \multirow{2}{*}{LPA-Met} & NT & 81.70\% & 0.7844 \\  & NFT-MEP & 84.18\% & 0.7928 \\ \hline \multirow{2}{*}{PHY-Kno} & NT & 82.25\% & 0.7191 \\  & NFT-MEP & 88.15\% & 0.7579 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison result between the NT and NFT-MEP models to predict hourly PM\({}_{10}\) with three classes of output data. 79.40% and 90.83%. In addition, the NFT-MEP model had the highest average F-score with a value between 0.6253 and 0.8183 and the highest average MCC between 0.5318 and 0.7395. The LSTM showed an inferior model performance to the NFT-MEP model, while the ARIMA and ARIMAX showed the lowest model performance mainly because they cannot classify Class 2 and Class 3. In addition, the results for the five classes of output data were similar to those of the three classes of output data. The results showed that the NFT-MEP model had the highest statistic indicators. The average accuracy of the NFT-MEP model was between 67.40% and 83.31%. In addition, the average F-score was between 0.5001 and 0.7255, and the average MCC was between 0.6778 and 0.4983. The LSTM had a higher model performance than the other two models. The four models were used to predict hourly PM\({}_{2.5}\) with three and five classes of output data similar to PM\({}_{10}\), which are reported in Table 7. The results showed that the NFT-MEP model had the highest three statistic indicators compared to the three other models similar to the PM\({}_{10}\) model. The average accuracy of the NFT-MEP model for the three classes of output data was between 81.45 and 85.28%. In addition, the average F-score was between 0.7824 and 0.7851, and the average MCC was between 0.6847 and 0.6920. The average accuracy of the NFT-MEP model for five classes of output data was between 73.76% and 76.16%. In addition, the average F-score was between 0.7229 and 0.7285 and the average MCC was between 0.6515 and 0.6632. For both types of output data, the LSTM had an inferior model performance and the other two models had the lowest model performance. As evidenced by the experimental results, the NFT-MEP model had the highest model performance. The LSTM had an inferior model performance, while ARIMA and ARIMAX had the lowest model performance.