A Novel Hybrid Framework for Hourly PM2.5 Concentration Forecasting Using CEEMDAN and Deep Temporal Convolutional Neural Network

TL;DR

This paper introduces a hybrid forecasting model combining CEEMDAN and DeepTCN to improve hourly PM2.5 concentration predictions by effectively modeling complex data patterns, outperforming existing models in accuracy.

Contribution

The study presents a novel hybrid framework integrating CEEMDAN and DeepTCN for enhanced PM2.5 forecasting, demonstrating superior accuracy over traditional models.

Findings

CEEMDAN-DeepTCN achieves the highest forecasting accuracy.

The model effectively captures data patterns of pollutants and meteorological factors.

Improves PM2.5 concentration prediction performance.

Abstract

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 modelling 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 time series model, artificial neural network, and the popular deep learning 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.