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.
- PM 2.5 concentration prediction
- deep learning
- AC-LSTM network
- attention mechanism