24-Hour prediction of PM2.5 concentrations by combining empirical mode decomposition and bidirectional long short-term memory neural network

Abstract

Accurate prediction of the future PM2.5 concentration is crucial to human health and ecological environmental protection. Nowadays, deep learning methods show advantages in the prediction of PM2.5 concentration, but few of the studies can achieve accurate prediction of short term (within 6 h) concentration and also catch longer term (6–24 h) change trends. To address this issue, this study constructs a novel hybrid prediction model by combining the empirical mode decomposition (EMD) method, sample entropy (SE) index and bidirectional long and short-term memory neural network (BiLSTM) to predict 0–24 h PM2.5 concentrations. The experimental results show that the hybrid model has good performance on PM2.5 prediction with R2 = 0.987, RMSE = 2.77 μg/m3 at T + 1 moment and R2 = 0.904, RMSE = 7.51 μg/m3 at T + 6 moment. The novel model improves the accuracy on short-term (within 6 h) prediction of PM2.5 concentrations by at least 50% compared with other single deep learning models. Moreover, it well catches the variation trend of PM2.5 concentrations after 6 h till 24 h.