Enhanced short-term prediction of urban PM2.5 concentrations by improved hybrid deep learning

Abstract

Purpose

The aim of this study was to investigate the impact of improved deep learning model on the predictive performance of PM_2.5 concentration.

Methods

We developed a new model combining one-dimensional convolutional neural network and bidirectional long short-term memory neural network to predict PM_2.5 concentrations at hourly intervals. The air pollution observation data from 2020 to 2022 collected at several national air quality monitoring stations in Shenyang (Liaoning province, China) were employed to train our model. The performance of the proposed model was boosted by connecting the layer of network calculated results with the PM_2.5 sequence data. Furthermore, data of most relevant air quality monitoring stations and PM_2.5 feature factors of the target station were screened. The spatial correlation of major air pollutant and the interaction between PM_2.5 and other pollutant factors were therefore considered to improve the accuracy of the model.

Results

The root mean square error, mean absolute error, mean absolute percentage error of the new method were reduced by 49%, 51%, 44% and the R² was improved by 4.6% respectively compared with the control group for the next hour prediction. The proposed improvement method can reduce the prediction error of the model in the next 6 h.

Conclusions

In this study, the proposed model improvement method can significantly reduce the error of the model in predicting PM_2.5 concentration. The proposed method can improve the model in the next 6 h prediction accuracy. This study provides a new perspective for establishing high-precision models for PM_2.5 prediction.