Please use this identifier to cite or link to this item: http://hdl.handle.net/10995/103064
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dc.contributor.authorSubbotina, I. E.en
dc.contributor.authorBuevich, A. G.en
dc.contributor.authorSergeev, A. P.en
dc.contributor.authorShichkin, A. V.en
dc.contributor.authorBaglaeva, E. M.en
dc.contributor.authorRemezova, M. S.en
dc.date.accessioned2021-08-31T15:07:15Z-
dc.date.available2021-08-31T15:07:15Z-
dc.date.issued2020-
dc.identifier.citationTwo-step combined algorithm for improving the accuracy of predicting methane concentration in atmospheric air based on the narx neural network and subsequent prediction of residuals / I. E. Subbotina, A. G. Buevich, A. P. Sergeev, et al. — DOI 10.25283/2223-4594-2020-2-59-67 // Arktika: Ekologia i Ekonomika. — 2020. — Vol. 38. — Iss. 2. — P. 59-67.en
dc.identifier.issn22234594-
dc.identifier.otherFinal2
dc.identifier.otherAll Open Access, Gold3
dc.identifier.otherhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85101779592&doi=10.25283%2f2223-4594-2020-2-59-67&partnerID=40&md5=51a317926bf4e5e3cbae08e60aa2a224
dc.identifier.otherhttp://eng.arctica-ac.ru/docs/journals/38/dvuhshagovyy-kombinirovannyy-algoritm-povysheniya-tochnosti-prognozirovaniya-kon.pdfm
dc.identifier.urihttp://hdl.handle.net/10995/103064-
dc.description.abstractClimate change in the Arctic is great and can have a significant inverse effect on the global climate, which determines the global significance of climate change in the Arctic. To date, many issues regarding the mechanisms responsible for the rapid melting of Arctic ice and permafrost degradation have not been resolved. It is not known when and what consequences these changes will lead to. Assessing the relationship between global warming and greenhouse gas emissions is an important environmental challenge. Among the main greenhouse gases, the evolution and climate-forming role of the carbon dioxide have been studied. The data on the methane subcycle of the carbon cycle is much less. In the paper, the authors propose a two-step combined algorithm (NARXR) to improve the accuracy of predicting methane concentration in atmospheric air based on the NARX neural network and subsequent prediction of the residuals. Two commonly used models based on artificial neural networks (ANN) for predicting time series are compared to determine the most appropriate base model. Nonlinear autoregressive neural network with external input (NARX) and Elman’s neural network are used. For the forecast, the authors use data on the methane concentration (CH4) in the atmospheric surface layer on the Arctic Island of Bely (Russia). Data is selected for a time interval of 192 hours, because it is characterized by significant daily fluctuations in the concentration of CH4. Values corresponding to the first 168 hours of the interval are used to train the ANN, and then concentrations are predicted for the next 24 hours. The proposed approach shows more accurate forecast results. © Subbotina I. E., Buevich A. G., Sergeev A. P., Shichkin A. V., Baglaeva E. M., Remezova M. S., 2020.en
dc.description.sponsorshipThe authors are grateful to the Department of Science and Innovation of the Yamal-Nenets Autonomous District and to the NP Russian Center for the Development of the Arctic, city of Salekhard, for technical and logistical support of scientific expeditions to the Island of Bely. The authors also thank the reviewers for constructive criticism and useful recommendations that have improved the quality of article materials.en
dc.format.mimetypeapplication/pdfen
dc.language.isoruen
dc.publisherNuclear Safety Institute of the Russian Academy of Sciencesen
dc.rightsinfo:eu-repo/semantics/openAccessen
dc.sourceArkt. Ekologia Ekonomika2
dc.sourceArktika: Ekologia i Ekonomikaen
dc.subjectARTIFICIAL NEURAL NETWORKSen
dc.subjectGREENHOUSE GASESen
dc.subjectNARXen
dc.subjectRESIDUALSen
dc.titleTwo-step combined algorithm for improving the accuracy of predicting methane concentration in atmospheric air based on the narx neural network and subsequent prediction of residualsen
dc.titleДвухшаговый комбинированный алгоритм повышения точности прогнозирования концентрации метана в атмосферном воздухе на основе нейронной сети NARX и последующего прогнозирования невязокru
dc.typeArticleen
dc.typeinfo:eu-repo/semantics/articleen
dc.typeinfo:eu-repo/semantics/publishedVersionen
dc.identifier.doi10.25283/2223-4594-2020-2-59-67-
dc.identifier.scopus85101779592-
local.contributor.employeeSubbotina, I.E., Institute of Industrial Ecology Ural Branch of the RАS, 20, Sofia Kovalevskaya str, Yekaterinburg, 620990, Russian Federation
local.contributor.employeeBuevich, A.G., Institute of Industrial Ecology Ural Branch of the RАS, 20, Sofia Kovalevskaya str, Yekaterinburg, 620990, Russian Federation
local.contributor.employeeSergeev, A.P., Institute of Industrial Ecology Ural Branch of the RАS, 20, Sofia Kovalevskaya str, Yekaterinburg, 620990, Russian Federation
local.contributor.employeeShichkin, A.V., Institute of Industrial Ecology Ural Branch of the RАS, 20, Sofia Kovalevskaya str, Yekaterinburg, 620990, Russian Federation
local.contributor.employeeBaglaeva, E.M., Institute of Industrial Ecology Ural Branch of the RАS, 20, Sofia Kovalevskaya str, Yekaterinburg, 620990, Russian Federation
local.contributor.employeeRemezova, M.S., Ural Federal University named after the first President of Russia B. N. Yeltsin, Ekaterinburg, Russian Federation
local.description.firstpage59-
local.description.lastpage67-
local.issue2-
local.volume38-
local.contributor.departmentInstitute of Industrial Ecology Ural Branch of the RАS, 20, Sofia Kovalevskaya str, Yekaterinburg, 620990, Russian Federation
local.contributor.departmentUral Federal University named after the first President of Russia B. N. Yeltsin, Ekaterinburg, Russian Federation
local.identifier.pure13189404-
local.identifier.pure66f1e11b-8f88-4e24-a09e-3f964d97b219uuid
local.identifier.eid2-s2.0-85101779592-
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