# PM2.5 Concentration Prediction Based on CNN-BiLSTM and Attention Mechanism  
Jinsong Zhang  
School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, China; [EMAIL_ADDRESS] (J.Z. ); [EMAIL_ADDRESS] (Y.P. ); [EMAIL_ADDRESS] (B.R.) Yongtao Peng  
School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, China; [EMAIL_ADDRESS] (J.Z. ); [EMAIL_ADDRESS] (Y.P. ); [EMAIL_ADDRESS] (B.R.) Bo Ren  
School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, China; [EMAIL_ADDRESS] (J.Z. ); [EMAIL_ADDRESS] (Y.P. ); renbo@dlmu. Multiscale Air Quality-Advanced Plume Treatment model to predict the PM2.5 concentration caused by aviation activities. [PERSON] et al. [9] employed the nested-grid GEOS-Chem model and satellite data of MODIS and MISR instruments to predict PM2.5 concentration. However, due to excessive consumption of resources and manpower, this method has certain shortcomings. The statistical method, including machine learning, deep learning, or other statistical methods, has been widely used for predicting PM2.5 concentration. They overcome the shortcomings of the physical model, such as the Markov model [10], support the vector regression model [11], alternating decision trees, and random forests [12]. However, many meteorological factors related to PM2.5 are nonlinear. The above machine learning methods used to predict PM2.5 concentration are used for dealing with linear relationships and show low prediction accuracy. Recently, some deep learning models, including convolutional neural networks (CNN), recurrent neural networks (RNN), and their deformations, have been adopted for predicting PM2.5 concentration. CNN can extract valid information from feature inputs and discovery deep connections between different feature elements [13]. RNN and its deformations are very effective for processing data with sequence characteristics. They can mine the timing information and semantic information from data [14; 15; 16]. Therefore, these models can handle non-linear relationships well and make up for the defects of machine learning among these models. The long-short term memory (LSTM) model is relatively popular. It is suitable for processing and predicting important events with relatively long intervals and delays in time series data. In addition, the bidirectional long short-term memory neural network (BiLSTM) connects two hidden layers and operates in both directions between input and output. The BiLSTM-based structure also allows the training of the prediction model to use both the future features and the past features for a specific time range efficiently, which improves the prediction accuracy to a certain extent. Additionally, BiLSTM is very popular in text classification [17], speech recognition [18], and PM2.5 prediction [19]. Meanwhile, its extended model, CNN-BiLSTM, is also widely used in many fields, such as diagnosis of heart disease [20], video compression [21], and COVID-19 diagnosis [22]. However, this model requires a large number of training data and cannot reflect the influence of different features on the prediction results, especially for predicting PM2.5 concentration. At present, most methods based on the integration of CNN and LSTM do not take it into consideration. Therefore, the attention mechanism can be introduced into the time series models to capture the importance degree of the effects of featured states at different times in the past on future PM2.5 concentration. The attention-based layer can automatically weight the past feature states to improve prediction accuracy, as shown in [17; 23]. Therefore, a hybrid model named CNN-BiLSTM-Attention is proposed, including a CNN layer, a BiLSTM layer, and an attention layer. It can utilize CNN to extract effective spatial features from all factors related to PM2.5. BiLSTM is employed to solve the problems of gradient disappearance and explosion in the way of time series and identify temporal features in two directions of the hidden layer [24].