# An Improved Hybrid Transfer Learning-Based Deep Learning Model for PM2.5 Concentration Prediction
## 3 Experiments  
### Experiment Setting and Data Source  
The dataset used in this paper was collected by Microsoft Research's Urban Air project [35]. We select datasets related to Beijing and Tianjin to evaluate the proposed TL-DSTP-DANN neural network. The distribution of monitoring sites in Beijing and Tianjin is shown in Figure 3, where the red points are all the sites in Beijing, and the black point is the Tianjin site to be predicted. For reasons of data collection, the historical data of the Tianjin site are only one month, and the historical data for the Beijing sites are from 1 May 2014 to 30 April 2015 (1 year, 8759 data). Because the data of Tianjin site are very few, the training effect is not good if they are directly used. This paper investigates how to use data migration learning from all sites in Beijing to predict PM2.5 concentrations at the Tianjin site. The dataset collects air quality records from 36 sites in Beijing and 1 site in Tianjin. Each air quality record contains six pollutants: PM2.5, PM10, SO\({}_{2}\), NO\({}_{2}\), CO and O\({}_{3}\). Each weather record contains seven items: time, weather, temperature, pressure, humidity, wind speed, and wind direction. Table 1 shows the details of the datasets used in this paper. There are very complex relationships among these factors [28]. It is the main reason why the deep learning-based method is used in this study. The first 75% of the Tianjin site is selected as the training data, and the remaining 25% as the test data. For data from the Beijing stations, because PM2.5 data from individual stations are highly correlated, consecutive missing values greater than one row are filled in using the IDW interpolation [36] method based on the PM2.5 concentrations at adjacent stations. If the consecutive missing values are less than two rows, the linear interpolation method is used. For the data from the Tianjin site, linear interpolation is used directly. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Attribute** & **Source Site** & **Target Site** \\ \hline District & Beijing of China & Tianjin of China \\ Time Range & 1 May 2014–30 April 2015 & 30 March 2015–30 April 2015 \\ Number of Sample & 8759 & 745 \\ Number of Site & 36 & 1 \\ Standard Deviation & 81.23 & 49.78 \\ Mean Value & 84.73 & 76.66 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Dataset summary. For proving the effectiveness of the proposed method for stations with fewer data, the TL-DSTP-DANN network is used to model the data and predict the PM2.5 concentration for the 3 rd hour in the future. Appropriate hyperparameters are set to the model to produce the best performance. The prediction time step is set to 8. In order to use all source domain data and target domain training data, the batch sizes are set to 275 and 18 for the source and target domain data, respectively. A back-propagation algorithm is used to train all models, with regression losses for PM2.5 prediction as MSE loss functions and losses for domain label classification defined as dichotomous cross-entropy loss functions. During training, small-batch stochastic gradient descent is used combined with the Adam optimizer, setting the upper limit of the training period to 120, and the learning rate to 0.001. Each attention module uses a layer of LSTM network, where the hidden state of LSTM network is set to the same, that is, \(m=p=q=128\). In order to improve the prediction accuracy, we use the minimum-maximum normalization method given in the formula for normalization:  
\[x=\frac{x-min}{max-min} \tag{28}\]  
For evaluating the effectiveness of the method, three metrics are used in the experiment, including the root mean square error (RMSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE).