Forecasting Fine Particulate Matter Concentrations by In-Depth Learning Model According to Random Forest and Bilateral Long- and Short-Term Memory Neural Networks

Accurate prediction of fine particulate matter concentration in the future is important for human health due to the necessity of an early warning system. Generally, deep learning methods, when widely used, perform better in forecasting the concentration of PM_2.5. However, the source information is limited, and the dynamic process is uncertain. The method of predicting short-term (3 h) and long-term trends has not been achieved. In order to deal with the issue, the research employed a novel mixed forecasting model by coupling the random forest (RF) variable selection and bidirectional long- and short-term memory (BiLSTM) neural net in order to forecast concentrations of PM_2.5/0~12 h. Consequently, the average absolute percentage error of 1, 6, and 12 h shows that the PM_2.5 concentration prediction is 3.73, 9.33, and 12.68 μg/m³ for Beijing, 1.33, 3.38, and 4.60 μg/m³ for Guangzhou, 1.37, 4.19, and 6.35 μg/m³ for Xi’an, and 2.20, 7.75, and 10.07 μg/m³ for Shenyang, respectively. Moreover, the results show that the suggested mixed model is an advanced method that can offer high accuracy of PM_2.5 concentrations from 1 to 12 h post.