A Hybrid CNN-LSTM Model for Forecasting Particulate Matter (PM2.5)

• Published in: IEEE access Vol. 8; pp. 26933 - 26940
• Main Authors: Li, Taoying, Hua, Miao, Wu, Xu
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Air quality; Artificial neural networks; Atmospheric modeling; CNN; Data models; Deep learning; Feature extraction; Forecasting; LSTM; Mathematical models; Multivariate analysis; Neural networks; Periodic variations; PM2.5 concentration prediction; Pollutants; Predictive models; Root-mean-square errors; Time series analysis
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.2971348

PM2.5 is one of the most important pollutants related to air quality, and the increase of its concentration will aggravate the threat to people's health. Therefore, the prediction of surface PM2.5 concentration is of great significance to human health protection. In this study, A hybrid CNN-LSTM model is developed by combining the convolutional neural network (CNN) with the long short-term memory (LSTM) neural network for forecasting the next 24h PM2.5 concentration in Beijing, which makes full use of their advantages that CNN can effectively extract the features related to air quality and the LSTM can reflect the long term historical process of input time series data. The air quality data of the last 7days and the PM2.5 concentration of the next day are first set as the input and output of the model due to the periodicity, respectively. Subsequently four models namely univariate LSTM model, multivariate LSTM model, univariate CNN-LSTM model and multivariate CNN-LSTM model, are established for PM2.5 concentration prediction. Finally, mean absolute error (MAE) and root mean square error (RMSE) are employed to evaluate the performance of these models and results show that the proposed multivariate CNN-LSTM model performs the best results due to low error and short training time.