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dc.creatorStamenković, Lidija J.
dc.creatorAntanasijević, Davor
dc.creatorRistić, Mirjana
dc.creatorPerić-Grujić, Aleksandra
dc.creatorPocajt, Viktor
dc.date.accessioned2021-03-10T12:45:37Z
dc.date.available2021-03-10T12:45:37Z
dc.date.issued2015
dc.identifier.issn0352-5139
dc.identifier.urihttp://TechnoRep.tmf.bg.ac.rs/handle/123456789/2997
dc.description.abstractThe aim of this study was to develop a model for forecasting CH4 emissions at the national level, using artificial neural networks (ANN) with broadly available sustainability, economical and industrial indicators as their inputs. ANN modeling was performed using two different types of architecture; a backpropagation neural network (BPNN) and a general regression neural network (GRNN). A conventional multiple linear regression (MLR) model was also developed in order to compare the model performance and assess which model provides the best results. ANN and MLR models were developed and tested using the same annual data for 20 European countries. The ANN model demonstrated very good performance, significantly better than the MLR model. It was shown that a forecast of CH4 emissions at the national level using the ANN model could be made successfully and accurately for a future period of up to two years, thereby opening the possibility to apply such a modeling technique, which could be used to support the implementation of sustainable development strategies and environmental management policies.en
dc.publisherSrpsko hemijsko društvo, Beograd
dc.relationinfo:eu-repo/grantAgreement/MESTD/Basic Research (BR or ON)/172007/RS//
dc.rightsopenAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.sourceJournal of the Serbian Chemical Society
dc.subjectnational emissionen
dc.subjectgeneral regression neural networken
dc.subjectbackpropagation neural networken
dc.subjectmultiple linear regressionen
dc.titleModeling of methane emissions using the artificial neural network approachen
dc.typearticle
dc.rights.licenseBY-NC-ND
dc.citation.epage433
dc.citation.issue3
dc.citation.other80(3): 421-433
dc.citation.rankM23
dc.citation.spage421
dc.citation.volume80
dc.identifier.doi10.2298/JSC020414110S
dc.identifier.fulltexthttp://TechnoRep.tmf.bg.ac.rs/bitstream/id/9384/0352-51391400110S.pdf
dc.identifier.scopus2-s2.0-84930654706
dc.identifier.wos000353423600011
dc.type.versionpublishedVersion


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