In The Science of the total environment ; h5-index 0.0
Air pollution is one of the serious environmental problems that humankind faces and also a hot topic in Northeastern Asia. Therefore, the accurate prediction of PM2.5 (particulate matter with an aerodynamic diameter of ≤2.5 μm) is very significant in the management of human health and the decision-making of government for the environmental management. In this study, a spatiotemporal convolutional neural network (CNN) and long short-term (LSTM) memory (CNN-LSTM) model (also called PM (particulate matter) predictor) was proposed and used to predict the next day's daily average PM2.5 concentration in Beijing City. The spatiotemporal correlation analysis using the mutual information (MI) was performed, considering not only the linear correlation but also nonlinear correlation between target and observation parameters; in addition, it was fully considered for the whole area of China with the target monitoring station as the center and also for the historic air quality and meteorological data. As a result, the spatiotemporal feature vector (STFV) which reflects both linear and nonlinear correlations between parameters was effectively constructed. The PM predictor secured a fast and accurate prediction performance by efficiently extracting the inherent features of the latent air quality and meteorological input data associated with PM2.5 through CNN and by fully reflecting the long-term historic process of input time series data through LSTM. The air quality and meteorological data from the 384 monitoring stations which represents the whole area of China with Beijing City as the center during the 3 years (Jan. 1st, 2015 to Dec. 31th, 2017) were used to verify the validity of the proposed method. In conclusion, the proposed method was proved to have a better stability and prediction performance compared to multi-layer perceptron (MLP) and LSTM models.
Pak Unjin, Ma Jun, Ryu Unsok, Ryom Kwangchol, Juhyok U, Pak Kyongsok, Pak Chanil
CNN, Deep learning, LSTM, PM predictor, PM2.5 prediction, Spatiotemporal correlation