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Research and application of novel Euler polynomial-driven grey model for short-term PM10 forecasting

Xiwang Xiang (Southwest University of Science and Technology, Mianyang, China)
Xin Ma (Southwest University of Science and Technology, Mianyang, China)
Minda Ma (Department of Earth System Science, Tsinghua University, Beijing, China)
Wenqing Wu (Southwest University of Science and Technology, Mianyang, China)
Lang Yu (Southwest University of Science and Technology, Mianyang, China)

Grey Systems: Theory and Application

ISSN: 2043-9377

Article publication date: 21 October 2020

Issue publication date: 18 June 2021

196

Abstract

Purpose

PM10 is one of the most dangerous air pollutants which is harmful to the ecological system and human health. Accurate forecasting of PM10 concentration makes it easier for the government to make efficient decisions and policies. However, the PM10 concentration, particularly, the emerging short-term concentration has high uncertainties as it is often impacted by many factors and also time varying. Above all, a new methodology which can overcome such difficulties is needed.

Design/methodology/approach

The grey system theory is used to build the short-term PM10 forecasting model. The Euler polynomial is used as a driving term of the proposed grey model, and then the convolutional solution is applied to make the new model computationally feasible. The grey wolf optimizer is used to select the optimal nonlinear parameters of the proposed model.

Findings

The introduction of the Euler polynomial makes the new model more flexible and more general as it can yield several other conventional grey models under certain conditions. The new model presents significantly higher performance, is more accurate and also more stable, than the six existing grey models in three real-world cases and the case of short-term PM10 forecasting in Tianjin China.

Practical implications

With high performance in the real-world case in Tianjin China, the proposed model appears to have high potential to accurately forecast the PM10 concentration in big cities of China. Therefore, it can be considered as a decision-making support tool in the near future.

Originality/value

This is the first work introducing the Euler polynomial to the grey system models, and a more general formulation of existing grey models is also obtained. The modelling pattern used in this paper can be used as an example for building other similar nonlinear grey models. The practical example of short-term PM10 forecasting in Tianjin China is also presented for the first time.

Keywords

Acknowledgements

This research work was supported by the National Natural Science Foundation of China (No. 71901184, 71771033, 71571157), the Humanities and Social Science Fund of Ministry of Education of China (No. 19YJCZH119), Sichuan Province Undergraduate Training Programs for Innovation and Entrepreneurship (No. S201910619005S), the Grey System Theme Innovation Zone (No. GS2019017) and National Statistical Scientific Research Project (2018LY42).

Citation

Xiang, X., Ma, X., Ma, M., Wu, W. and Yu, L. (2021), "Research and application of novel Euler polynomial-driven grey model for short-term PM10 forecasting", Grey Systems: Theory and Application, Vol. 11 No. 3, pp. 498-517. https://doi.org/10.1108/GS-02-2020-0023

Publisher

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Emerald Publishing Limited

Copyright © 2020, Emerald Publishing Limited

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