An ensemble long short-term memory neural network for hourly PM2.5 concentration forecasting

To protect public health by providing an early warning, PM2.5 concentration forecasting is an essential and effective work. In this paper, an ensemble long short-term memory neural network (E-LSTM) is proposed for hourly PM2.5 concentration forecasting. The presented model is implemented using three steps: (1) ensemble empirical mode decomposition (EEMD) is firstly utilized for multi-modal feature extraction, (2) long short-term memory approach (LSTM) is then employed for multi-modal feature learning, and (3) inverse EEMD computation is finally used for multi-modal feature estimated integration. In each modeling process, the mode information of the PM2.5 and the corresponding meteorological variables in 1-h advance are utilized as inputs to forecast the next mode information of the PM2.5 concentration. To evaluate the performance of the E-LSTM model, two datasets collected from two environmental monitoring stations in Beijing, China, are investigated. It is demonstrated that the E-LSTM model inspired by ensemble learning, which constructs multiple LSTMs in different modes, obtained better forecasting performance than that using the single LSTM and feed forward neural network in terms of mean absolute percentage error (19.604% and 16.929%), root mean square error (12.077 μg m-3 and 13.983 μg m-3), and correlation coefficient criteria (0.994 and 0.991) respectively.