Support vector machines for oil classification link with polyaromatic hydrocarbon contamination in the environment.
Water Sci Technol
; 83(5): 1039-1054, 2021 Mar.
Article
en En
| MEDLINE
| ID: mdl-33724935
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.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Hidrocarburos Policíclicos Aromáticos
/
Contaminación por Petróleo
Tipo de estudio:
Prognostic_studies
País/Región como asunto:
Asia
Idioma:
En
Revista:
Water Sci Technol
Asunto de la revista:
SAUDE AMBIENTAL
/
TOXICOLOGIA
Año:
2021
Tipo del documento:
Article
Pais de publicación:
ENGLAND
/
ESCOCIA
/
GB
/
GREAT BRITAIN
/
INGLATERRA
/
REINO UNIDO
/
SCOTLAND
/
UK
/
UNITED KINGDOM