Support vector machines for oil classification link with polyaromatic hydrocarbon contamination in the environment
The main focus of this study is exploring the spatial distribution of polyaromatics hydrocarbon links between oil spills in the environment via Support Vector Machines based on Kernel-Radial Basis Function (RBF) approach for high precision classification of oil spill type from its sample fingerprint...
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IWA Publishing
2021
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oai:doaj.org-article:377836402cbe48bb8436f2879ef3704b2021-11-06T10:50:42ZSupport vector machines for oil classification link with polyaromatic hydrocarbon contamination in the environment0273-12231996-973210.2166/wst.2021.038https://doaj.org/article/377836402cbe48bb8436f2879ef3704b2021-03-01T00:00:00Zhttp://wst.iwaponline.com/content/83/5/1039https://doaj.org/toc/0273-1223https://doaj.org/toc/1996-9732The main focus of this study is exploring the spatial distribution of polyaromatics hydrocarbon links between oil spills in the environment via Support Vector Machines based on Kernel-Radial Basis Function (RBF) approach for high precision classification of oil spill type from its sample fingerprinting in Peninsular Malaysia. The results show the highest concentrations of Σ Alkylated PAHs and Σ EPA PAHs in ΣTAH concentration in diesel from the oil samples PP3_liquid and GP6_Jetty achieving 100% classification output, corresponding to coherent decision boundary and projective subspace estimation. The high dimensional nature of this approach has led to the existence of a perfect separability of the oil type classification from four clustered oil type components; i.e diesel, bunker C, Mixture Oil (MO), lube oil and Waste Oil (WO) with the slack variables of ξ ≠ 0. Of the four clusters, only the SVs of two are correctly predicted, namely diesel and MO. The kernel-RBF approach provides efficient and reliable oil sample classification, enabling the oil classification to be optimally performed within a relatively short period of execution and a faster dataset classification where the slack variables ξ are non-zero.Azimah IsmailHafizan JuahirSaiful Bahri MohamedMohd Ekhwan TorimanAzlina Md. KassimSharifuddin Md. ZainHadieh MonajemiWan Kamaruzaman Wan AhmadMunirah Abdul ZaliAnanthy RetnamMohd. Zaki Mohd. TaibMazlin MokhtarSiti Nor Fazillah AbdullahIWA Publishingarticleclassifierfingerprintsoil classificationsupport vector machinesEnvironmental technology. Sanitary engineeringTD1-1066ENWater Science and Technology, Vol 83, Iss 5, Pp 1039-1054 (2021) |
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| collection |
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| topic |
classifier fingerprints oil classification support vector machines Environmental technology. Sanitary engineering TD1-1066 |
| spellingShingle |
classifier fingerprints oil classification support vector machines Environmental technology. Sanitary engineering TD1-1066 Azimah Ismail Hafizan Juahir Saiful Bahri Mohamed Mohd Ekhwan Toriman Azlina Md. Kassim Sharifuddin Md. Zain Hadieh Monajemi Wan Kamaruzaman Wan Ahmad Munirah Abdul Zali Ananthy Retnam Mohd. Zaki Mohd. Taib Mazlin Mokhtar Siti Nor Fazillah Abdullah Support vector machines for oil classification link with polyaromatic hydrocarbon contamination in the environment |
| description |
The main focus of this study is exploring the spatial distribution of polyaromatics hydrocarbon links between oil spills in the environment via Support Vector Machines based on Kernel-Radial Basis Function (RBF) approach for high precision classification of oil spill type from its sample fingerprinting in Peninsular Malaysia. The results show the highest concentrations of Σ Alkylated PAHs and Σ EPA PAHs in ΣTAH concentration in diesel from the oil samples PP3_liquid and GP6_Jetty achieving 100% classification output, corresponding to coherent decision boundary and projective subspace estimation. The high dimensional nature of this approach has led to the existence of a perfect separability of the oil type classification from four clustered oil type components; i.e diesel, bunker C, Mixture Oil (MO), lube oil and Waste Oil (WO) with the slack variables of ξ ≠ 0. Of the four clusters, only the SVs of two are correctly predicted, namely diesel and MO. The kernel-RBF approach provides efficient and reliable oil sample classification, enabling the oil classification to be optimally performed within a relatively short period of execution and a faster dataset classification where the slack variables ξ are non-zero. |
| format |
article |
| author |
Azimah Ismail Hafizan Juahir Saiful Bahri Mohamed Mohd Ekhwan Toriman Azlina Md. Kassim Sharifuddin Md. Zain Hadieh Monajemi Wan Kamaruzaman Wan Ahmad Munirah Abdul Zali Ananthy Retnam Mohd. Zaki Mohd. Taib Mazlin Mokhtar Siti Nor Fazillah Abdullah |
| author_facet |
Azimah Ismail Hafizan Juahir Saiful Bahri Mohamed Mohd Ekhwan Toriman Azlina Md. Kassim Sharifuddin Md. Zain Hadieh Monajemi Wan Kamaruzaman Wan Ahmad Munirah Abdul Zali Ananthy Retnam Mohd. Zaki Mohd. Taib Mazlin Mokhtar Siti Nor Fazillah Abdullah |
| author_sort |
Azimah Ismail |
| title |
Support vector machines for oil classification link with polyaromatic hydrocarbon contamination in the environment |
| title_short |
Support vector machines for oil classification link with polyaromatic hydrocarbon contamination in the environment |
| title_full |
Support vector machines for oil classification link with polyaromatic hydrocarbon contamination in the environment |
| title_fullStr |
Support vector machines for oil classification link with polyaromatic hydrocarbon contamination in the environment |
| title_full_unstemmed |
Support vector machines for oil classification link with polyaromatic hydrocarbon contamination in the environment |
| title_sort |
support vector machines for oil classification link with polyaromatic hydrocarbon contamination in the environment |
| publisher |
IWA Publishing |
| publishDate |
2021 |
| url |
https://doaj.org/article/377836402cbe48bb8436f2879ef3704b |
| work_keys_str_mv |
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