A Novel Linear Time-Varying GM(1,N) Model for Forecasting Haze: A Case Study of Beijing, China

• Main Authors: Pingping Xiong, Jia Shi, Lingling Pei, Song Ding
• Format: Article
• Language: English
• Published: MDPI AG 2019-07-01
• Series: Sustainability
• Subjects: haze; linear time-varying GM(1,N) model; interval grey number; Beijing; forecasting
• Online Access: https://www.mdpi.com/2071-1050/11/14/3832

Haze is the greatest challenge facing China’s sustainable development, and it seriously affects China’s economy, society, ecology and human health. Based on the uncertainty and suddenness of haze, this paper proposes a novel linear time-varying grey model (GM)(1,N) based on interval grey number sequences. Because the original GM(1,N) model based on interval grey number sequences has constant parameters, it neglects the dynamic change characteristics of parameters over time. Therefore, this novel linear time-varying GM(1,N) model, based on interval grey number sequences, is established on the basis of the original GM(1,N) model by introducing a linear time polynomial. To verify the validity and practicability of this model, this paper selects the data of PM₁₀, SO₂ and NO₂ concentrations in Beijing, China, from 2008 to 2018, to establish a linear time-varying GM(1,3) model based on interval grey number sequences, and the prediction results are compared with the original GM(1,3) model. The result indicates that the prediction effect of the novel model is better than that of the original model. Finally, this model is applied to forecast PM₁₀ concentration for 2019 to 2021 in Beijing, and the forecast is made to provide a reference for the government to carry out haze control.