A novel GM(1, N) model based on interval gray number and its application to research on smog pollution. Issue 3 (14th June 2019)

• Record Type: Journal Article
• Authors: Xiong, Pingping; He, Zhiqing; Chen, Shiting; Peng, Mao
• Is Part Of: Kybernetes. Volume 49:Issue 3(2020)
• Page Start: 753
• Page End: 778
• Publication Date: 2019-06-14
• Subjects: Prediction -- Gray system theory -- Kernel and degree of grayness -- Possibility function -- GM(1, N) model; Cybernetics -- Periodicals; Systems engineering -- Periodicals; 003.505
• DOI: 10.1108/K-12-2018-0694
• Languages: English
• ISSNs: 0368-492X

Abstract

Abstract : Purpose: In recent years, domestic smog has become increasingly frequent and the adverse effects of smog have increasingly become the focus of public attention. It is a way to analyze such problems and provide solutions by mathematical methods. Design/methodology/approach: This paper establishes a new gray model (GM) (1, N) prediction model based on the new kernel and degree of grayness sequences under the case that the interval gray number distribution information is known. First, the new kernel and degree of grayness sequences of the interval gray number sequence are calculated using the reconstruction definition of the kernel and degree of grayness. Then, the GM(1, N) model is formed based on the above new sequences to simulate and predict the kernel and degree of the grayness of the interval gray number sequence. Finally, the upper and lower bounds of the interval gray number are deduced based on the calculation formulas of the kernel and degree of grayness. Findings: To verify further the practical significance of the model proposed in this paper, the authors apply the model to the simulation and prediction of smog. Compared with the traditional GM(1, N) model, the new GM(1, N) prediction model established in this paper has better prediction effect and accuracy. Originality/value: This paper improves the traditional GM(1, N) prediction model and establishes a new GM(1, N) prediction model in the case of the known distribution information of the interval gray number of the smog pollutants concentrations data.