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A hybrid novel SVM model for predicting CO2 emissions using Multiobjective Seagull Optimization

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dc.citedby5
dc.contributor.authorEhteram M.en_US
dc.contributor.authorSammen S.S.en_US
dc.contributor.authorPanahi F.en_US
dc.contributor.authorSidek L.M.en_US
dc.contributor.authorid57113510800en_US
dc.contributor.authorid57192093108en_US
dc.contributor.authorid55368172500en_US
dc.contributor.authorid35070506500en_US
dc.date.accessioned2023-05-29T09:05:25Z
dc.date.available2023-05-29T09:05:25Z
dc.date.issued2021
dc.descriptionagricultural market; carbon dioxide; carbon emission; Gross Domestic Product; optimization; support vector machine; Iran; carbon dioxide; algorithm; gross national product; Iran; support vector machine; Algorithms; Carbon Dioxide; Gross Domestic Product; Iran; Support Vector Machineen_US
dc.description.abstractThe agricultural sector is one of the most important sources of CO2 emissions. Thus, the current study predicted CO2 emissions based on data from the agricultural sectors of 25 provinces in Iran. The gross domestic product (GDP), the square of the GDP (GDP2), energy use, and income inequality (Gini index) were used as the inputs. The study used support vector machine (SVM) models to predict CO2 emissions. Multiobjective algorithms (MOAs), such as the seagull optimization algorithm (MOSOA), salp swarm algorithm (MOSSA), bat algorithm (MOBA), and particle swarm optimization (MOPSO) algorithm, were used to perform three important tasks for improving the SVM models. Additionally, an inclusive multiple model (IMM) used the outputs of the MOSOA, MOSSA, MOBA, and MOPSO algorithms as the inputs for predicting CO2 emissions. It was observed that the best kernel function based on the SVM-MOSOA was the radial function. Additionally, the best input combination used all the gross domestic product (GDP), squared GDP (GDP2), energy use, and income inequality (Gini index) inputs. The results indicated that the quality of the obtained Pareto front based on the MOSOA was better than those of the other algorithms. Regarding the obtained results, the IMM model decreased the mean absolute errors of the SVM-MOSOA, SVM-MOSSA, SVM-MOBA, and SVM-PSO models by 24, 31, 69, and 76%, respectively, during the training stage. The current study showed that the IMM model was the best model for predicting CO2 emissions. � 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s11356-021-15223-4
dc.identifier.epage66192
dc.identifier.issue46
dc.identifier.scopus2-s2.0-85111525151
dc.identifier.spage66171
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85111525151&doi=10.1007%2fs11356-021-15223-4&partnerID=40&md5=e152e92eea718e13285060dce8208abc
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25885
dc.identifier.volume28
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleEnvironmental Science and Pollution Research
dc.titleA hybrid novel SVM model for predicting CO2 emissions using Multiobjective Seagull Optimizationen_US
dc.typeArticleen_US
dspace.entity.typePublication
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