{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T08:15:54Z","timestamp":1783584954885,"version":"3.55.0"},"reference-count":32,"publisher":"Emerald","issue":"3","license":[{"start":{"date-parts":[[2019,6,3]],"date-time":"2019-06-03T00:00:00Z","timestamp":1559520000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["K"],"published-print":{"date-parts":[[2019,6,3]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>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.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>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.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>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.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>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.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/k-12-2018-0694","type":"journal-article","created":{"date-parts":[[2019,6,14]],"date-time":"2019-06-14T09:23:55Z","timestamp":1560504235000},"page":"753-778","source":"Crossref","is-referenced-by-count":14,"title":["A novel GM(1,N) model based on interval gray number and its application to research on smog pollution"],"prefix":"10.1108","volume":"49","author":[{"given":"Pingping","family":"Xiong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiqing","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiting","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mao","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"140","reference":[{"issue":"10","key":"key2020022015440963000_ref001","first-page":"122","article-title":"Forecast of smog pollution based on big data analysis","volume":"42","year":"2017","journal-title":"Environmental Science and Management"},{"issue":"6","key":"key2020022015440963000_ref002","first-page":"150","article-title":"Fog and haze forecasting and analysis based on data mining","volume":"39","year":"2017","journal-title":"Manufacturing Automation"},{"key":"key2020022015440963000_ref003","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1016\/j.energy.2018.08.040","article-title":"A novel self-adapting intelligent grey model for forecasting China's natural-gas demand","volume":"162","year":"2018","journal-title":"Energy"},{"key":"key2020022015440963000_ref004","first-page":"749","article-title":"A novel discrete grey multivariable model and its application in forecasting the output value of China\u2019s high-tech industries","volume":"127C","year":"2019","journal-title":"Computers and Industrial Engineering"},{"key":"key2020022015440963000_ref005","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1016\/j.jclepro.2017.06.167","article-title":"Forecasting Chinese CO2 emissions from fuel combustion using a novel grey multivariable model","volume":"162","year":"2017","journal-title":"Journal of Cleaner Production"},{"key":"key2020022015440963000_ref006","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.buildenv.2018.03.058","article-title":"Prediction of PM2.5 concentration based on the similarity in air quality monitoring network","volume":"137","year":"2018","journal-title":"Building and Environment"},{"key":"key2020022015440963000_ref007","volume-title":"Industrial and Business Forecasting Method","year":"1982"},{"key":"key2020022015440963000_ref008","first-page":"1","volume-title":"Grey System and Its Application","year":"2010","edition":"5th ed"},{"issue":"2","key":"key2020022015440963000_ref009","first-page":"313","article-title":"Algorithn rules of interval grey numbers based on the \u2018Kernel\u2019 and the degree of greyness of grey numbers","volume":"32","year":"2010","journal-title":"Systems Engineering and Electronics"},{"key":"key2020022015440963000_ref010","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.apm.2017.12.010","article-title":"The kernel-based nonlinear multivariate grey model","volume":"56","year":"2018","journal-title":"Applied Mathematical Modelling"},{"key":"key2020022015440963000_ref011","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1016\/j.apm.2019.01.039","article-title":"The novel fractional discrete multivariate grey system model and its applications","volume":"70","year":"2019","journal-title":"Applied Mathematical Modelling"},{"issue":"12","key":"key2020022015440963000_ref012","first-page":"2190","article-title":"Kernal and greyness of interval grey number under known possibility function","volume":"32","year":"2017","journal-title":"Control and Decision"},{"key":"key2020022015440963000_ref013","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.eswa.2017.06.012","article-title":"An improved grey dynamic trend incident model with application to factors causing smog weather","volume":"87","year":"2017","journal-title":"Expert System with Applications"},{"issue":"5","key":"key2020022015440963000_ref014","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1108\/K-10-2013-0227","article-title":"A GM (1, N) \u2013 based economic cybernetics model for the high \u2013 tech industries in China","volume":"43","year":"2014","journal-title":"Kybernetes"},{"issue":"3","key":"key2020022015440963000_ref015","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1007\/s10614-015-9488-5","article-title":"A predictive analysis of clean energy consumption, economic growth and environmental regulation in China using an optimized grey dynamic model","volume":"46","year":"2015","journal-title":"Computational Economics"},{"key":"key2020022015440963000_ref016","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.jclepro.2018.10.010","article-title":"Modelling the nonlinear relationship between CO2 emissions and economic growth a PSO algorithm-based grey Verhulst model","volume":"207","year":"2019","journal-title":"Journal of Cleaner Production"},{"issue":"6","key":"key2020022015440963000_ref017","doi-asserted-by":"crossref","first-page":"931","DOI":"10.1108\/K-04-2015-0110","article-title":"Testing the trade relationships between China, Singapore, Malaysia and Thailand using grey Lotka-Volterra competition model","volume":"45","year":"2016","journal-title":"Kybernetes"},{"issue":"7","key":"key2020022015440963000_ref018","doi-asserted-by":"crossref","first-page":"1775","DOI":"10.1016\/j.cnsns.2012.11.017","article-title":"Grey system model with the fractional order accumulation","volume":"18","year":"2013","journal-title":"Communications in Nonlinear Science and Numerical Simulation"},{"key":"key2020022015440963000_ref019","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1016\/j.apm.2018.06.025","article-title":"Grey multivariable convolution model with new information priority accumulation","volume":"62","year":"2018","journal-title":"Applied Mathematical Modelling"},{"key":"key2020022015440963000_ref020","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1016\/j.apm.2017.07.010","article-title":"An improved seasonal rolling grey forecasting model using a cycle truncation accumulated generating operation for traffic flow","volume":"51","year":"2017","journal-title":"Applied Mathematical Modelling"},{"key":"key2020022015440963000_ref021","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1016\/j.asoc.2018.06.004","article-title":"Flexible job shop scheduling problem with interval grey processing time","volume":"70","year":"2018","journal-title":"Applied Soft Computing"},{"issue":"1","key":"key2020022015440963000_ref022","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1109\/JSEE.2015.00013","article-title":"Interval grey number sequence prediction by using non-homogenous exponential discrete grey forecasting model","volume":"26","year":"2015","journal-title":"Journal of Systems Engineering and Electronics"},{"key":"key2020022015440963000_ref023","first-page":"33","article-title":"Nonlinear multivariable GM(1,N) model based on interval gray number sequence","volume-title":"The Journal of Grey System","year":"2018"},{"key":"key2020022015440963000_ref024","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/j.cnsns.2017.06.004","article-title":"Grey-Markov prediction model based on background value optimization and Central-point triangular whitenization weight function","volume":"54","year":"2018","journal-title":"Communications in Nonlinear Science and Numerical Simulation"},{"issue":"10","key":"key2020022015440963000_ref025","first-page":"1831","article-title":"Grey prediction model of interval grey numbers based on axion of generalized non-decrease grey degree","volume":"31","year":"2016","journal-title":"Control and Decision"},{"key":"key2020022015440963000_ref026","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1016\/j.energy.2016.02.001","article-title":"Comparison of China's primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM(1,1) model","volume":"100","year":"2016","journal-title":"Energy"},{"key":"key2020022015440963000_ref027","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.energy.2018.03.045","article-title":"Forecasting the output of shale gas in China using an unbiased grey model and weakening buffer operator","volume":"151","year":"2018","journal-title":"Energy"},{"key":"key2020022015440963000_ref028","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1016\/j.cie.2018.02.042","article-title":"Improved multi-variable grey forecasting model with a dynamic background-value coefficient and its application","volume":"118","year":"2018","journal-title":"Computers and Industrial Engineering"},{"issue":"1","key":"key2020022015440963000_ref029","first-page":"1","article-title":"A self-adaptive intelligence gray prediction model with the optimal fractional order accumulating operator and its application","volume":"23","year":"2017","journal-title":"Mathematical Methods in the Applied Sciences"},{"key":"key2020022015440963000_ref030","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1016\/j.engappai.2016.08.007","article-title":"Development of an optimization method for the GM(1,N) model","volume":"55","year":"2016","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"key2020022015440963000_ref031","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1016\/j.scitotenv.2018.04.040","article-title":"Development of a stacked ensemble model for forecasting and analyzing daily average PM2.5 concentrations in Beijing, China","volume":"635","year":"2018","journal-title":"Science of the Total Environment"},{"issue":"1","key":"key2020022015440963000_ref032","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1109\/JSEE.2014.00009","article-title":"Optimization approach of background value and initial item for improving prediction precision of GM(1,1) model","volume":"25","year":"2014","journal-title":"Journal of Systems Engineering and Electronics"}],"container-title":["Kybernetes"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/K-12-2018-0694\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/K-12-2018-0694\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T21:51:29Z","timestamp":1753393889000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/k\/article\/49\/3\/753-778\/267126"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,3]]},"references-count":32,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2019,6,3]]}},"alternative-id":["10.1108\/K-12-2018-0694"],"URL":"https:\/\/doi.org\/10.1108\/k-12-2018-0694","relation":{},"ISSN":["0368-492X","0368-492X"],"issn-type":[{"value":"0368-492X","type":"print"},{"value":"0368-492X","type":"print"}],"subject":[],"published":{"date-parts":[[2019,6,3]]}}}