Urban PM_2.5 concentration prediction via attention-based CNN-LSTM

Urban particulate matter forecasting is regarded as an essential issue for early warning and control management of air pollution, especially fine particulate matter (PM _2.5). However, existing methods for PM _2.5 concentration prediction neglect the effects of featured states at different times in the past on future PM _2.5 concentration, and most fail to effectively simulate the temporal and spatial dependencies of PM _2.5 concentration at the same time. With this consideration, we propose a deep learning-based method, AC-LSTM, which comprises a one-dimensional convolutional neural network (CNN), long short-term memory (LSTM) network, and attention-based network, for urban PM _2.5 concentration prediction. Instead of only using air pollutant concentrations, we also add meteorological data and the PM _2.5 concentrations of adjacent air quality monitoring stations as the input to our AC-LSTM. Hence, the spatiotemporal correlation and interdependence of multivariate air quality-related time-series data are learned by the CNN-LSTM network in AC-LSTM. The attention mechanism is applied to capture the importance degrees of the effects of featured states at different times in the past on future PM _2.5 concentration. The attention-based layer can automatically weigh the past feature states to improve prediction accuracy. In addition, we predict the PM _2.5 concentrations over the next 24 h by using air quality data in Taiyuan city, China, and compare it with six baseline methods. To compare the overall performance of each method, the mean absolute error (MAE), root-mean-square error (RMSE), and coecient of determination (R ²) are applied to the experiments in this paper. The experimental results indicate that our method is capable of dealing with PM _2.5 concentration prediction with the highest performance.