Support vector machines for oil classification link with polyaromatic hydrocarbon contamination in the environment

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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Bibliographic Details
Main Authors: 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
Format: Article
Language:English
Published: IWA Publishing 2021-03-01
Series:Water Science and Technology
Subjects:
classifier
fingerprints
oil classification
support vector machines
Online Access:http://wst.iwaponline.com/content/83/5/1039

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