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: Kiat, P.E., Malek, M.A., Shamsuddin, S.M.
Format: Article
Language:English
Published: 2020
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spelling my.uniten.dspace-131892020-07-03T08:34:04Z Artificial intelligence projection model for methane emission from livestock in Sarawak Kiat, P.E. Malek, M.A. Shamsuddin, S.M. 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. © 2019 Penerbit Universiti Kebangsaan Malaysia. All rights reserved. 2020-02-03T03:30:59Z 2020-02-03T03:30:59Z 2019 Article 10.17576/jsm-2019-4807-02 en
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
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. © 2019 Penerbit Universiti Kebangsaan Malaysia. All rights reserved.
format Article
author Kiat, P.E.
Malek, M.A.
Shamsuddin, S.M.
spellingShingle Kiat, P.E.
Malek, M.A.
Shamsuddin, S.M.
Artificial intelligence projection model for methane emission from livestock in Sarawak
author_facet Kiat, P.E.
Malek, M.A.
Shamsuddin, S.M.
author_sort Kiat, P.E.
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
publishDate 2020
_version_ 1672614214120767488
score 13.223943