# PM2.5 Concentration Prediction Based on CNN-BiLSTM and Attention Mechanism
## 3 Experiment
### Results and Discussion
#### 3.3.2 Long-Term Forecast with Multi-Source Data  
In addition, a long-term PM2.5 concentration prediction is carried out to fully reflect the robustness of the proposed model, such as 72 h, 96 h, 120 h, and 144 h. Figure 12 indicates that when the PM2.5 concentration value fluctuates greatly, the proposed model can still track the general trend very well, but the deviation will be relatively large in small fluctuations. This can be attributed to a long time of prediction. The longer the time, the correlation between the features extracted by the model and the PM2.5 concentration will decrease, and the prediction error will increase. In order to show the superiority of the proposed model for predicting PM2.5 concentration in more detail, long-term predictions have also been made for all comparative models. The MAE and RMSE are still used as evaluation indicators. The results are shown in Tables 6 and 7. When predicting the PM2.5 concentration in the next 24 h, although CNN-LSTM is smaller than CNN-BiLSTM-Attention on MAE, it is larger than CNN-BiLSTM-Attention on RMSE. Additionally, the CNN-BiLSTM-Attention model is optimal at 48 h, whether on MAE or RMSE. As the prediction time increases, the relevance of the data decreases, and the influence of other variables on the current PM2.5 concentration is reduced, the weight of important features extracted by attention is also affected. Therefore, the prediction accuracy will decrease. However, the proposed model still performs better than the other six models, which can be seen from Table 6. Even if the proposed model is used to predict the next 144 h, it is more accurate than machine learning at predicting the  
Figure 11: Comparisons of predicted and observed values of each model. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **MAE** & **RMSE** & \(R^{2}\) \\ \hline CNN-BiLSTM-Attention & 2.366 & 3.095 & 0.960 \\ CNN-BiLSTM & 3.851 & 5.333 & 0.912 \\ CNN-LSTM & 5.577 & 7.032 & 0.906 \\ XGBOOST & 7.607 & 8.541 & 0.890 \\ SVR & 7.796 & 8.549 & 0.858 \\ Ridge Regression & 7.695 & 9.032 & 0.878 \\ Lasso Regression & 7.845 & 9.781 & 0.844 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: MAE, RMSE (Unit: \(\mathrm{\SIUnitSymbolMicro g}/\mathrm{m}^{3}\) ), and \(R^{2}\) of the proposed model and the comparison model. next 48 h. Therefore, this model can solve the problem of long term PM2.5 concentration prediction. Based on the above results, we summarize this study as follows:  
(1) Firstly, we obtain multi-source data that are composed of PM2.5 concentration values, meteorological data, and air quality data in hourly units and sourced from the U.S. Embassy in Beijing. There are missing values in the dataset due to uncontrollable \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Models** & **24** & **48** & **72** & **96** & **120** & **144** \\ \hline CNN-BiLSTM-Attention & 4.228 & 2.366 & 3.777 & 6.036 & 6.337 & 6.918 \\ CNN-BiLSTM & 4.437 & 3.851 & 5.652 & 6.788 & 6.797 & 7.452 \\ CNN-LSTM & 3.837 & 5.577 & 7.234 & 7.671 & 9.148 & 12.384 \\ XGBOOST & 5.275 & 7.607 & 7.928 & 10.554 & 12.744 & 13.318 \\ SVR & 7.762 & 7.796 & 10.486 & 11.102 & 11.405 & 11.834 \\ Ridge Regression & 7.832 & 7.695 & 11.502 & 12.364 & 13.649 & 14.823 \\ Lasso Regression & 7.845 & 7.845 & 11.464 & 11.775 & 12.779 & 13.914 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: MAE for long-term prediction of each model (Unit: \(\text{\mu g}/\text{m}^{3}\)). Figure 12: Long-term prediction of PM2.5 concentration by CNN-BiLSTM-Attention model.