Artificial intelligence projection model for methane emission from livestock in Sarawak

Artificial 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, s...

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Main Authors:	Peng, Eng Kiat, Marlinda Abdul Malek, Siti Mariyam Shamsuddin
Format:	Article
Language:	English
Published:	Penerbit Universiti Kebangsaan Malaysia 2019
Online Access:	http://journalarticle.ukm.my/13726/ http://journalarticle.ukm.my/13726/ http://journalarticle.ukm.my/13726/1/02%20Peng%20Eng%20Kiat.pdf

id	ukm-13726
recordtype	eprints
spelling	ukm-137262019-11-29T09:10:34Z http://journalarticle.ukm.my/13726/ Artificial intelligence projection model for methane emission from livestock in Sarawak Peng, Eng Kiat Marlinda Abdul Malek, Siti Mariyam Shamsuddin, Artificial 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. Penerbit Universiti Kebangsaan Malaysia 2019-07 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/13726/1/02%20Peng%20Eng%20Kiat.pdf Peng, Eng Kiat and Marlinda Abdul Malek, and Siti Mariyam Shamsuddin, (2019) Artificial intelligence projection model for methane emission from livestock in Sarawak. Sains Malaysiana, 48 (7). pp. 1325-1332. ISSN 0126-6039 http://www.ukm.my/jsm/malay_journals/jilid48bil7_2019/KandunganJilid48Bil7_2019.html
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Kebangasaan Malaysia
building	UKM Institutional Repository
collection	Online Access
language	English
description	Artificial 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.
format	Article
author	Peng, Eng Kiat Marlinda Abdul Malek, Siti Mariyam Shamsuddin,
spellingShingle	Peng, Eng Kiat Marlinda Abdul Malek, Siti Mariyam Shamsuddin, Artificial intelligence projection model for methane emission from livestock in Sarawak
author_facet	Peng, Eng Kiat Marlinda Abdul Malek, Siti Mariyam Shamsuddin,
author_sort	Peng, Eng Kiat
title	Artificial intelligence projection model for methane emission from livestock in Sarawak
title_short	Artificial intelligence projection model for methane emission from livestock in Sarawak
title_full	Artificial intelligence projection model for methane emission from livestock in Sarawak
title_fullStr	Artificial intelligence projection model for methane emission from livestock in Sarawak
title_full_unstemmed	Artificial intelligence projection model for methane emission from livestock in Sarawak
title_sort	artificial intelligence projection model for methane emission from livestock in sarawak
publisher	Penerbit Universiti Kebangsaan Malaysia
publishDate	2019
url	http://journalarticle.ukm.my/13726/ http://journalarticle.ukm.my/13726/ http://journalarticle.ukm.my/13726/1/02%20Peng%20Eng%20Kiat.pdf
first_indexed	2023-09-18T20:05:30Z
last_indexed	2023-09-18T20:05:30Z
_version_	1777407130767196160

Artificial intelligence projection model for methane emission from livestock in Sarawak

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