Three-hourly PM2.5 and O3 concentrations prediction based on time series decomposition and LSTM model with attention mechanism

Abstract

In recent years, air quality has attracted wide attention from all over the world, among which the high concentration of particulate matter with an aerodynamic less than 2.5 μm (PM2.5) and ozone (O3) has a great adverse impact on human health and daily life. Previous studies on two pollutant predictions are overly concerned with model improvement but hardly focus on influence variable screening, and pollutant's time series features extraction and identification. To better improve the prediction accuracy and enhance the application of the model in practice, in the present study, a novel model RF-CEEMDAN-Attention-LSTM was proposed, which has three processes: (1) random forest (RF) screened out highly correlated influence variables; (2) complete ensemble empirical mode decomposition with adaptive noise (CEEMADN) method was adopted to decompose the PM2.5 and O3 concentration time series into multiple sub-time sequences; (3) the double hidden layer LSTM model with attention and dropout mechanism captured nonlinear relationships and dynamic changes of time series features. Three-hourly PM2.5 and O3 concentration in Chengdu were used to validate the effectiveness of the developed RF-CEEMDAN-Attention-LSTM model by comparing five other parallel models. The final results showed that the model not only had a better fitting effect on both PM2.5 and O3 than other comparable models in the entire timeline, but the model also had the highest R2 (PM2.5:0.916, O3:0.525) values for these two air pollutants at high concentration values.