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...

Ausführliche Beschreibung

Bibliographische Detailangaben
Veröffentlicht in:	Water science and technology : a journal of the International Association on Water Pollution Research. - 1986. - 83(2021), 5 vom: 15. März, Seite 1039-1054
1. Verfasser:	Ismail, Azimah (VerfasserIn)
Weitere Verfasser:	Juahir, Hafizan, Mohamed, Saiful Bahri, Toriman, Mohd Ekhwan, Kassim, Azlina Md, Zain, Sharifuddin Md, Monajemi, Hadieh, Ahmad, Wan Kamaruzaman Wan, Zali, Munirah Abdul, Retnam, Ananthy, Taib, Mohd Zaki Mohd, Mokhtar, Mazlin, Abdullah, Siti Nor Fazillah
Format:	Online-Aufsatz
Sprache:	English
Veröffentlicht:	2021
Zugriff auf das übergeordnete Werk:	Water science and technology : a journal of the International Association on Water Pollution Research
Schlagworte:	Journal Article Hydrocarbons Polycyclic Aromatic Hydrocarbons

Beschreibung
Zusammenfassung:	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
Beschreibung:	Date Completed 18.03.2021 Date Revised 18.03.2021 published: Print Citation Status MEDLINE
ISSN:	0273-1223
DOI:	10.2166/wst.2021.038