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Tsinghua Science and Technology 2024, 29(1): 99-111 https://doi.org/10.26599/TST.2022.9010060
Open Access | Issue | Published: 21 August 2023

A Hybrid Air Quality Prediction Model Based on Empirical Mode Decomposition

Show Author's Information Yuxuan Cao1,  Difei Zhang2,  Shaoqi Ding1,  Weiyi Zhong1,  Chao Yan1,3( )
School of Computer Science, Qufu Normal University, Rizhao 276826, China
School of Mathematical Sciences, Qufu Normal University, Qufu 273165, China
College of Economic and Management, Shandong University of Science and Technology, Qingdao 250307, China
Keywords:
AutoRegressive Integrated Moving Average (ARIMA), Singular Value Decomposition (SVD), air quality prediction, Empirical Mode Decomposition (EMD)
Cite this article:
Cao Y, Zhang D, Ding S, et al. A Hybrid Air Quality Prediction Model Based on Empirical Mode Decomposition. Tsinghua Science and Technology, 2024, 29(1): 99-111. https://doi.org/10.26599/TST.2022.9010060
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Abstract Full text About this article

Air pollution is a severe environmental problem in urban areas. Accurate air quality prediction can help governments and individuals make proper decisions to cope with potential air pollution. As a classic time series forecasting model, the AutoRegressive Integrated Moving Average (ARIMA) has been widely adopted in air quality prediction. However, because of the volatility of air quality and the lack of additional context information, i.e., the spatial relationships among monitor stations, traditional ARIMA models suffer from unstable prediction performance. Though some deep networks can achieve higher accuracy, a mass of training data, heavy computing, and time cost are required. In this paper, we propose a hybrid model to simultaneously predict seven air pollution indicators from multiple monitoring stations. The proposed model consists of three components: (1) an extended ARIMA to predict matrix series of multiple air quality indicators from several adjacent monitoring stations; (2) the Empirical Mode Decomposition (EMD) to decompose the air quality time series data into multiple smooth sub-series; and (3) the truncated Singular Value Decomposition (SVD) to compress and denoise the expanded matrix. Experimental results on the public dataset show that our proposed model outperforms the state-of-art air quality forecasting models in both accuracy and time cost.


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About this article

A Hybrid Air Quality Prediction Model Based on Empirical Mode Decomposition

Show Author's information Yuxuan Cao1, Difei Zhang2, Shaoqi Ding1, Weiyi Zhong1, Chao Yan1,3( )
School of Computer Science, Qufu Normal University, Rizhao 276826, China
School of Mathematical Sciences, Qufu Normal University, Qufu 273165, China
College of Economic and Management, Shandong University of Science and Technology, Qingdao 250307, China

Abstract

Air pollution is a severe environmental problem in urban areas. Accurate air quality prediction can help governments and individuals make proper decisions to cope with potential air pollution. As a classic time series forecasting model, the AutoRegressive Integrated Moving Average (ARIMA) has been widely adopted in air quality prediction. However, because of the volatility of air quality and the lack of additional context information, i.e., the spatial relationships among monitor stations, traditional ARIMA models suffer from unstable prediction performance. Though some deep networks can achieve higher accuracy, a mass of training data, heavy computing, and time cost are required. In this paper, we propose a hybrid model to simultaneously predict seven air pollution indicators from multiple monitoring stations. The proposed model consists of three components: (1) an extended ARIMA to predict matrix series of multiple air quality indicators from several adjacent monitoring stations; (2) the Empirical Mode Decomposition (EMD) to decompose the air quality time series data into multiple smooth sub-series; and (3) the truncated Singular Value Decomposition (SVD) to compress and denoise the expanded matrix. Experimental results on the public dataset show that our proposed model outperforms the state-of-art air quality forecasting models in both accuracy and time cost.

Keywords: AutoRegressive Integrated Moving Average (ARIMA), Singular Value Decomposition (SVD), air quality prediction, Empirical Mode Decomposition (EMD)

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Received: 18 September 2022
Revised: 15 November 2022
Accepted: 26 November 2022
Published: 21 August 2023
Issue date: February 2024

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