1. Support vector machines for oil classification link with polyaromatic hydrocarbon contamination in the environment.
- Author
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Ismail A, Juahir H, Mohamed SB, Toriman ME, Kassim AM, Zain SM, Monajemi H, Ahmad WKW, Zali MA, Retnam A, Taib MZM, Mokhtar M, and Abdullah SNF
- Subjects
- Hydrocarbons, Malaysia, Support Vector Machine, Petroleum Pollution, Polycyclic Aromatic Hydrocarbons
- Abstract
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.
- Published
- 2021
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