# An Improved Hybrid Transfer Learning-Based Deep Learning Model for PM2.5 Concentration Prediction
## 5 Conclusions and Future Work  
In this paper, a dual-stage two-phase model and an adversarial domain adaptation hybrid transfer learning strategy are proposed to predict PM2.5 concentration, especially for new sites with relatively little historical data. Firstly, the maximum mean discrepancy (MMD) is introduced into the proposed model to select the most suitable source domain site. Then, inputting data from the source domain and the target domain together into an improved DSTP model, the DSTP model extracts the spatial-temporal characteristics of both. DANN finds domain invariant features between source domain and target domain by fusing extracted spatial-temporal features. Finally, the PM2.5 concentration in the target domain is predicted by a regression predictor. To evaluate the performance of the proposed model, we use air quality data from the Beijing sites to assist in predicting PM2.5 concentrations at the Tianjin and Guangzhou sites. The main experimental results are as follows: (1) Compared with other transfer learning prediction models (including TL-LSTM, TL-BiLSTM, TL-DSTP), the proposed TL-DSTP-DANN model decreases by more than 8.5% in MAE, RMSE and MAPE; (2) Transfer learning can obviously improve the performance of PM2.5 prediction in newly built monitoring stations with insufficient data; (3) The comprehensive experimental results of the improved DSTP model combined with DANN are better than those of CNN, LSTM, and BiLSTM. Compared with the BiLSTM, the MAE of the improved DSTP model decreases by 12.05%, and the MAPE decreases by 20%.  
In our future work: (1) The current dataset contains only historical air pollutant concentrations and meteorological data, lacking relatively important geographical data, but geographical factors have an impact on PM2.5 concentrations. In the future, the proposed method could provide higher prediction accuracy if datasets containing geographical information are available; (2) An algorithm needs to be investigated to determine which of the multiple source domain data are most suitable for transfer learning to the target domain; (3) Whether the method proposed in this paper can be used to predict other air pollutants, such as O\({}_{3}\) and SO\({}_{2}\).  
Funding acquisition, J.N.; Project administration, J.N. and P.S.; Writing--original draft, Y.C.; Writing--review and editing, X.F. and Y.G. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (61873086), and the Science and Technology Support Program of Changzhou (CE20215022).  
Not applicable.  
Figure 7: The experimental results corresponding to different time steps.  
Data Availability Statement: Publicly available datasets were analyzed in this study. This data can be found here: [[https://www.microsoft.com/en-us/research/project/urban-air](https://www.microsoft.com/en-us/research/project/urban-air)]([https://www.microsoft.com/en-us/research/project/urban-air](https://www.microsoft.com/en-us/research/project/urban-air)), accessed on 1 December 2021.