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 PM_2.5 concentration is crucial to human health and ecological environmental protection. Nowadays, deep learning methods show advantages in the prediction of PM_2.5 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 PM_2.5 concentrations. The experimental results show that the hybrid model has good performance on PM_2.5 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 PM_2.5 concentrations by at least 50% compared with other single deep learning models. Moreover, it well catches the variation trend of PM_2.5 concentrations after 6 h till 24 h.

Keywords: Bidirectional long and short-term memory neural network; Deep learning; Empirical mode decomposition; PM(2.5).