Forecasting hourly PM2.5 based on deep temporal convolutional neural network and decomposition method

For hourly PM2.5 concentration prediction, accurately capturing the data patterns of external factors that affect PM2.5 concentration changes, and constructing a forecasting model is one of efficient means to improve forecasting accuracy. In this study, a novel hybrid forecasting model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and deep temporal convolutional neural network (DeepTCN) is developed to predict PM2.5 concentration, by modeling the data patterns of historical pollutant concentrations data, meteorological data, and discrete time variables' data. Taking PM2.5 concentration of Beijing as the sample, experimental results showed that the forecasting accuracy of the proposed CEEMDAN-DeepTCN model is verified to be the highest when compared with the statistics-based models, traditional machine learning models, the popular deep learning models and several existing hybrid models. The new model has improved the capability to model the PM2.5-related factor data patterns, and can be used as a promising tool for forecasting PM2.5 concentrations. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

The study developed a novel hybrid forecasting model based on CEEMDAN and DeepTCN for predicting PM2.5 concentrations, which showed improved forecasting accuracy compared to other traditional and deep learning models. The new model demonstrated enhanced capability in capturing the data patterns of PM2.5-related factors, making it a promising tool for PM2.5 concentration forecasting.