###### Abstract  
The air quality prediction is a very important and challenging task, especially PM\({}_{2.5}\) (particles with diameter less than 2.5 \(\upmu\)m) concentration prediction. To improve the accuracy of the PM\({}_{2.5}\) concentration prediction, an improved integrated deep neural network method based on attention mechanism is proposed in this paper. Firstly, the influence of exogenous series of other sites on the central site is considered to determine the best relevant site. Secondly, the data of all relevant sites are input into the improved dual-stage two-phase (DSTP) model, then the PM\({}_{2.5}\) prediction result of each site is obtained. Finally, with the PM\({}_{2.5}\) prediction result of each site, the attention-based layer predicts the PM\({}_{2.5}\) concentration at the central site. The experimental results show that the proposed model is superior to most of the latest models. PM\({}_{2.5}\) concentration prediction; deep neural network; attention mechanism; long short term memory; machine learning +
Footnote †: journal: Applied sciences  
the ability to express complex functions is limited. The generalization ability for complex prediction problems is limited. Recently, a deep learning method has been widely used for air quality prediction and many research results have been presented [17; 18]. For example, [PERSON] et al. [19] used a recurrent neural network (RNN) model to predict the concentration of air pollutants on subway platforms. [PERSON] et al. [20] used the long short-term memory-fully connected (LSTM-FC) neural network to extract spatio-temporal characteristics from historical air quality and meteorological data of the target and neighboring sites, where only the fully connected layer is used as the spatial combinator, ignoring the PM\({}_{2.5}\) concentration correlation between neighboring sites and the central site. [PERSON] et al. [21] presented a combined prediction scheme based on convolutional neural network (CNN) and long short-term memory (LSTM), where CNN is used to compress input data to eliminate redundancy, the spatial correlation between data is determined, and the temporal dependence between pollutants is studied by LSTM. In this scheme, the accuracy of the model will be negatively affected by unrelated sites when data from all neighboring sites are entered into the model, and all input features are treated equally and cannot focus on the important features. [PERSON] et al. [22] proposed a dual-stage attention-based recurrent neural network (DA-RNN), where the attention mechanism is not only in the input stage of the decoder, but also in the encoder stage so that the most relevant input features can be adaptively selected. However, in this method, a single-layer attention module is used in the encoder stage, and the weight learned is decentralized, which means this method is not effective for long term prediction. To deal with the shortcomings of the method in [22], [PERSON] et al. [23] proposed a dual-stage two-phase attention-based recurrent neural network (DSTP-RNN), where the two-phase attention mechanism is adopted in the encoder stage, and the stable attention weight can be obtained. However, only one site's data are used in this method, without considering the impact of other sites' data on the model. To deal with the problems in the deep learning based models introduced above and to further improve the effectiveness and accuracy of air quality prediction, an improved attention-based integrated deep neural network method is proposed, which is used to predict the PM\({}_{2.5}\) (particles with diameter less than 2.5 \(\upmu\)m) concentration. In this proposed method, an exogenous series correlation method is used to compute the relationship between the target series and the exogenous series, and the PM\({}_{2.5}\) concentration is predicted in the improved dual-stage two-phase (DSTP) model.