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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| Format: | Article |
| Language: | English |
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Penerbit Universiti Kebangsaan Malaysia
2019
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| Online Access: | http://journalarticle.ukm.my/13726/ http://journalarticle.ukm.my/13726/1/02%20Peng%20Eng%20Kiat.pdf |
| _version_ | 1848813357777289216 |
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| author | Peng, Eng Kiat Marlinda Abdul Malek, Siti Mariyam Shamsuddin, |
| author_facet | Peng, Eng Kiat Marlinda Abdul Malek, Siti Mariyam Shamsuddin, |
| author_sort | Peng, Eng Kiat |
| building | UKM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T00:16:55Z |
| format | Article |
| id | oai:generic.eprints.org:13726 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T00:16:55Z |
| publishDate | 2019 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| recordtype | eprints |
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| spelling | oai:generic.eprints.org: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 |
| spellingShingle | Peng, Eng Kiat Marlinda Abdul Malek, Siti Mariyam Shamsuddin, Artificial intelligence projection model for methane emission from livestock in Sarawak |
| title | 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_short | Artificial intelligence projection model for methane emission from livestock in Sarawak |
| title_sort | artificial intelligence projection model for methane emission from livestock in sarawak |
| url | http://journalarticle.ukm.my/13726/ http://journalarticle.ukm.my/13726/ http://journalarticle.ukm.my/13726/1/02%20Peng%20Eng%20Kiat.pdf |