An Ensemble Model with Adaptive Variational Mode Decomposition and Multivariate Temporal Graph Neural Network for PM2.5 Concentration Forecasting

Yadong Pei, Chiou-Jye Huang (), Yamin Shen and Yuxuan Ma
Additional contact information
Yadong Pei: Key Laboratory of Public Big Data Security Technology, Chongqing College of Mobile Communication, Chongqing 401420, China
Chiou-Jye Huang: College of Chemistry and Chemical Engineering and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen 361005, China
Yamin Shen: School of Information Science and Technology, Donghua University, Shanghai 201620, China
Yuxuan Ma: School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000, China

Sustainability, 2022, vol. 14, issue 20, 1-22

Abstract: Accurate prediction of PM2.5 concentration for half a day can provide valuable guidance for urban air pollution prevention and daily travel planning. In this paper, combining adaptive variational mode decomposition (AVMD) and multivariate temporal graph neural network (MtemGNN), a novel PM2.5 prediction model named PMNet is proposed. Some studies consider using VMD to stabilize time series but ignore the problem that VMD parameters are difficult to select, so AVMD is proposed to solve the appealing problem. Effective correlation extraction between multivariate time series affects model prediction accuracy, so MtemGNN is used to extract complex non-Euclidean distance relationships between multivariate time series automatically. The outputs of AVMD and MtemGNN are integrated and fed to the gate recurrent unit (GRU) to learn the long-term and short-term dependence of time series. Compared to several baseline models—long short-term memory (LSTM), GRU, and StemGNN—PMNet has the best prediction performance. Ablation experiments show that the Mean Absolute Error (MAE) is reduced by 90.141%, 73.674%, and 40.556%, respectively, after adding AVMD, GRU, and MtemGNN to the next 12-h prediction.

Keywords: prediction of PM2.5 concentration for half a day; adaptive variable mode decomposition; multivariate temporal graph neural network; gate recurrent unit (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/20/13191/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/20/13191/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:20:p:13191-:d:942038

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().