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Prediction of CO2 emission for the central European countries through five metaheuristic optimization techniques helping multilayer perceptron

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dc.citedby4
dc.contributor.authorMoayedi H.en_US
dc.contributor.authorMukhtar A.en_US
dc.contributor.authorAlshammari S.en_US
dc.contributor.authorBoujelbene M.en_US
dc.contributor.authorElbadawi I.en_US
dc.contributor.authorThi Q.T.en_US
dc.contributor.authorMirzaei M.en_US
dc.contributor.authorid55923628500en_US
dc.contributor.authorid57195426549en_US
dc.contributor.authorid57613571200en_US
dc.contributor.authorid57863001800en_US
dc.contributor.authorid56499091400en_US
dc.contributor.authorid58089337100en_US
dc.contributor.authorid58971283000en_US
dc.date.accessioned2025-03-03T07:47:23Z
dc.date.available2025-03-03T07:47:23Z
dc.date.issued2024
dc.description.abstractOne of the most significant issues in urban design is energy-related CO2 emissions, which are rising quickly as cities expand. The GDP of the Central European countries (from 1990 to 2016) based on several energy sources, such as coal, oil, natural gas, and renewable energy, are used as inputs in this study. To develop a reliable predictive network considering the problem complexity, multilayer perceptron (MLP) is combined with several nature-inspired optimization algorithms, namely, black hole algorithm (BHA), future search algorithm (FSA), backtracking search algorithm (BSA), biogeography-based optimization (BBO), and shuffled complex evolution (SCE). By applying the approaches mentioned above to the synthesis of the MLP, the recommended BBO, BHA, BSA, FSA, and SCE ensembles are obtained. A series of parametric studies are performed to improve the effectiveness of the employed models. It is found that, by combining the BBO, BHA, BSA, FSA, and SCE algorithms, the MLP's accuracy is increased. The result from this parametric analysis showed that SCE and BBO perform better than the other three algorithms as the CO2 emission was computed with the highest level of accuracy using R2 = 0.9999 and 0.9998, RMSE = 1.6781 and 2.0539 for SCE, and R2 = 0.9999 and 0.9998, RMSE = 1.8689 and 2.3833 for BBO. ? 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo2327437
dc.identifier.doi10.1080/19942060.2024.2327437
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85189426439
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85189426439&doi=10.1080%2f19942060.2024.2327437&partnerID=40&md5=57493739a1f07318cb237ae9bb822e25
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/37090
dc.identifier.volume18
dc.publisherTaylor and Francis Ltd.en_US
dc.relation.ispartofAll Open Access; Gold Open Access
dc.sourceScopus
dc.sourcetitleEngineering Applications of Computational Fluid Mechanics
dc.titlePrediction of CO2 emission for the central European countries through five metaheuristic optimization techniques helping multilayer perceptronen_US
dc.typeArticleen_US
dspace.entity.typePublication
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