# An Improved Hybrid Transfer Learning-Based Deep Learning Model for PM2.5 Concentration Prediction  
[PERSON]  
1 College of Internet of Things Engineering, Hohai University, Changzhou 213022, China;  
[EMAIL_ADDRESS] (YC. ); [EMAIL_ADDRESS] (Y.C. ); [EMAIL_ADDRESS] (X.F.) [PERSON]  
1 College of Internet of Things Engineering, Hohai University, Changzhou 213022, China;  
[EMAIL_ADDRESS] (YC. ); [EMAIL_ADDRESS] (Y.C. ); [EMAIL_ADDRESS] (X.F.) [PERSON]  
1 College of Internet of Things Engineering, Hohai University, Changzhou 213022, China;  
[EMAIL_ADDRESS] (YC. ); [EMAIL_ADDRESS] (Y.C. ); [EMAIL_ADDRESS] (X.F.) [PERSON]  
1 College of Internet of Things Engineering, Hohai University, Changzhou 213022, China;  
[EMAIL_ADDRESS] (YC. ); [EMAIL_ADDRESS] (Y.C. ); [EMAIL_ADDRESS] (X.F.) [PERSON]  
1 College of Internet of Things Engineering, Hohai University, Changzhou 213022, China;  
[EMAIL_ADDRESS] (YC. +++ ==WARNING: Truncated because of repetitions==
); [EMAIL_ADDRESS] (Y.C. ); [EMAIL_ADDRESS] (X.F.) Footnote 1: [https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)  
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model was better than that of other methods. [PERSON] et al. [12] proposed a stacked automatic encoder (SAE) model for air quality prediction and demonstrated that the model exhibited a better performance than linear models such as ARIMA. [PERSON] et al. [13] used the set of back-propagation neural networks (BP) to predict daily biomass combustion pollutant emissions. [PERSON] et al. [14] used the genetic algorithm (GA) combined with the artificial neural network (ANN) to predict local indoor air quality with two ventilation models. Although the nonlinear machine learning methods have achieved satisfactory performance in predicting air pollution, they are unable to learn from the long-term effects of air pollution, because their models are shallow networks with few model parameters. The generalization ability of these models to complex prediction problems is limited. To solve the problem of models with fewer parameters, people have started to use deep neural networks recently, which have been used widely in image processing, natural language understanding, and so forth [15; 16; 17; 18]. For example, [PERSON] et al. [19] proposed a multi-output multi-index supervised learning comprehensive prediction model (MMSL) based on long-term and short-term memory (LSTM) to predict the overall air quality in Beijing. [PERSON] et al. [20] used the CNN-LSTM model based on spatial-temporal clustering to predict the air quality of Beijing in multi-sites. Experiments show that CNN-LSTM and LSTM generally have a better performance than the BP neural network. [PERSON] et al. [21] proposed a method based on WRF/RNN to predict the air pollutants in Hangzhou over the next 24 h. [PERSON] et al. [22] proposed a dual-stage attention-based recurrent neural network (DA-RNN), where the attention mechanism is used in the input stage of the encoder and decoder, so that the most relevant input features can be selected adaptively. [PERSON] et al. [23] proposed a dual-stage two-phase attention-based recurrent neural network (DSTP-RNN), where a DSTP-based structure was used to enhance the spatial correlation of an exogenous series, and a two-stage attention mechanism was used to generate stable response weights. However, this method only uses the data of one site without considering the influence of the data of other sites on the model. To solve the above problems, in our previous work [24], an improved attention-based dual-stage two-phase fully connected (DSTP-FC) model was proposed to improve the accuracy of PM2.5 concentration prediction, where an exogenous series correlation method is used to calculate the relationship between the target series and the exogenous series, and the PM2.5 concentrations are predicted by a modified DSTP model. Although advanced deep learning methods can get good results in air quality prediction, these deep learning-based methods all need enough historical datasets to train the models.