# An Improved Hybrid Transfer Learning-Based Deep Learning Model for PM2.5 Concentration Prediction
For datasets with very little data, these methods do not provide very good prediction results. To solve the above data shortage problem, [PERSON] et al. [25] proposed a transfer learning-based bidirectional long short term memory (TL-BiLSTM) network to predict the air quality of new stations lacking data. This method transfers the knowledge learned from the existing air quality monitoring stations to the new monitoring stations to improve the prediction accuracy of the new stations. [PERSON] et al. [26] proposed a transfer learning model combining LSTM and RNN to predict the concentration of air pollutants. Their method inputs the data of all source domain sites into the model for pretraining, then adds the number of network layers to input the data of the target domain to train and predict the air quality of the target domain. [PERSON] et al. [27] proposed a hybrid deep migration learning strategy based on long and short-term memory (LSTM) and domain adversarial neural networks (DANN), where the temporal features of the source and target buildings are extracted by LSTM, and DANN is used to find the domain invariant features between the source and target buildings through domain adaptation. The above-mentioned methods have achieved satisfactory performance in the case of new site data shortages, but there are still some problems that should be further studied. For example, the temporal feature extractors of these models are all based on LSTM, which treat all input features equally and fail to pay attention to the important features. The TL-BiLSTM model is a single-site migration, and for the case where the source domain has multiple sites, it is not known which site in the source domain is selected for migration. The LSTM-RNN model inputs the data of all source domain sites into the model for pre-training, which is unsuitable when the number of source sites is large, because this method will input a lot of redundant data, resulting in the over-fitting and calculation problems [28]. To deal with these problems above, an improved hybrid transfer learning-based deep learning model is proposed in this paper for PM2.5 concentration prediction. When the amount of data in the target domain is small, the model cannot be well trained only by using the data in the target domain. If the transfer learning-based method is used, the model trained on the source domain data is not applicable to the target domain data, when the source and target domain data have different distributions. Thus, the motivation of this study is to use the domain adaptive migration learning method to find the domain invariant characteristics between the source domain and the target domain, and to use the data of the source domain and the target domain to predict the PM2.5 concentration in the target domain with fewer data. The main contributions of this paper are summarized as follows: (1) An improved hybrid transfer learning model with a dual-stage two-phase model (DSTP) and a domain adversarial neural network (DANN) is proposed; (2) The maximum mean discrepancy (MMD) is introduced into the air quality prediction based on transfer learning, which is used to select which station in the source domain is most suitable for migration to the target domain; (3) An improved dual-stage two-phase (DSTP) model is used to extract the spatial-temporal features of the source domain and the target domain. Various experiments on several cities in China are conducted, and the results verify the efficiency and the generalization ability of the proposed method. This paper is organized as follows: Section 2 describes the proposed method and presents the structure of the proposed deep learning-based model; Section 3 presents the experiments and results; Section 4 discusses the performance of different feature extractors, the generalization ability and the robustness of the proposed method, and the setting of hyperparameters; Section 5 provides the conclusion and possible future research directions.