Forecasting smog in Beijing using a novel time-lag GM (1, N) model based on interval grey number sequences
| dc.cclicence | CC-BY-NC | en |
| dc.contributor.author | Shi, Jia | |
| dc.contributor.author | Xiong, Pingping | |
| dc.contributor.author | Yang, Yingjie | |
| dc.contributor.author | Quan, Beichen | |
| dc.date.acceptance | 2020-12-22 | |
| dc.date.accessioned | 2022-05-12T12:26:51Z | |
| dc.date.available | 2022-05-12T12:26:51Z | |
| dc.date.issued | 2020-12-22 | |
| dc.description | The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. | en |
| dc.description.abstract | Purpose Smog seriously affects the ecological environment and poses a threat to public health. Therefore, smog control has become a key task in China, which requires reliable prediction. Design/methodology/approach This paper establishes a novel time-lag GM(1,N) model based on interval grey number sequences. Firstly, calculating kernel and degree of greyness of the interval grey number sequence respectively. Then, establishing the time-lag GM(1,N) model of kernel and degree of greyness sequences respectively to obtain their values after determining the time-lag parameters of two models. Finally, the upper and lower bounds of interval grey number sequences are obtained by restoring the values of kernel and degree of greyness. Findings In order to verify the validity and practicability of the model, the monthly concentrations of PM2.5, SO2 and NO2 in Beijing during August 2017 to September 2018 are selected to establish the time-lag GM(1,3) model for kernel and degree of greyness sequences respectively. Compared with three existing models, the proposed model in this paper has better simulation accuracy. Therefore, the novel model is applied to forecast monthly PM2.5 concentration for October to December 2018 in Beijing and provides a reference basis for the government to formulate smog control policies. Practical implications The proposed model can simulate and forecast system characteristic data with the time-lag effect more accurately, which shows that the time-lag GM(1,N) model proposed in this paper is practical and effective. Originality/value Based on interval grey number sequences, the traditional GM(1,N) model neglects the time-lag effect of driving terms, hence this paper introduces the time-lag parameters into driving terms of the traditional GM(1,N) model and proposes a novel time-lag GM(1,N) model. | en |
| dc.funder | No external funder | en |
| dc.funder.other | Royal Society | en |
| dc.identifier.citation | Shi, J., Xiong, P., Yang, Y. and Quan, B. (2021) Forecasting smog in Beijing using a novel time-lag GM (1, N) model based on interval grey number sequences. Grey Systems: Theory and Application,11(4), pp.754-778. | en |
| dc.identifier.doi | https://doi.org/10.1108/GS-02-2020-0025 | |
| dc.identifier.issn | 2043-9377 | |
| dc.identifier.uri | https://hdl.handle.net/2086/21864 | |
| dc.language.iso | en | en |
| dc.peerreviewed | Yes | en |
| dc.projectid | IEC\NSFC\170391 | en |
| dc.publisher | Emerald | en |
| dc.researchinstitute | Institute of Artificial Intelligence (IAI) | en |
| dc.subject | Smog | en |
| dc.subject | Time-lag GM(1,N) model | en |
| dc.subject | Interval grey number | en |
| dc.subject | Kernel and degree of greyness | en |
| dc.subject | Forecasting | en |
| dc.title | Forecasting smog in Beijing using a novel time-lag GM (1, N) model based on interval grey number sequences | en |
| dc.type | Article | en |