Publication:
Artificial intelligence projection model for methane emission from livestock in Sarawak

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dc.citedby5
dc.contributor.authorKiat P.E.en_US
dc.contributor.authorMalek M.A.en_US
dc.contributor.authorShamsuddin S.M.en_US
dc.contributor.authorid56181299200en_US
dc.contributor.authorid55636320055en_US
dc.contributor.authorid35619439200en_US
dc.date.accessioned2023-05-29T07:29:30Z
dc.date.available2023-05-29T07:29:30Z
dc.date.issued2019
dc.descriptionartificial intelligence; emission; emission inventory; fermentation; livestock; manure; methane; optimization; East Malaysia; Malaysia; Sarawak; Bos; Bubalus; Capra hircus; Cervidae; Ovis aries; Suidaeen_US
dc.description.abstractArtificial Intelligence is a topical trend employed to solve engineering and industrial problems by virtue of its abilities to deal with data uncertainty such as methane emissions. Hard computing methods are not suitable for determining the optimal emission in a methane emission data set. Instead, soft computing solutions should be considered in an effort to obtain better optimal solutions for industrial problems. This paper utilized the Guidelines provided in the 2006 Intergovernmental Panel on Climate Change (IPCC) to calculate and project methane emissions from selected six livestock in Sarawak, Malaysia. A particle swarm optimization (PSO) model was developed to project future methane emission by using number of livestock as the input parameter. The total CH4 inventory from the enteric fermentation of cattle, buffaloes, goats, sheep, swine and deer in Sarawak decreased from 1.860 to 1.856 Gg when calculation was carried out using the Tier 1 method. This decrease was due to population growth and the emission factors employed. Three statistical measures, root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were employed for evaluation. PSO has been shown to be able to give an accurate projection. The results of this study provide a benchmark information which can be used by the Sarawak government to develop appropriate policies and mitigation strategies to reduce future carbon footprint in the Sarawak livestock sector. � 2019 Penerbit Universiti Kebangsaan Malaysia. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.17576/jsm-2019-4807-02
dc.identifier.epage1332
dc.identifier.issue7
dc.identifier.scopus2-s2.0-85071450493
dc.identifier.spage1325
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85071450493&doi=10.17576%2fjsm-2019-4807-02&partnerID=40&md5=822e458f081c2d1bbcf45b32a2adc3c8
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24961
dc.identifier.volume48
dc.publisherPenerbit Universiti Kebangsaan Malaysiaen_US
dc.relation.ispartofAll Open Access, Bronze
dc.sourceScopus
dc.sourcetitleSains Malaysiana
dc.titleArtificial intelligence projection model for methane emission from livestock in Sarawaken_US
dc.typeArticleen_US
dspace.entity.typePublication
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