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

Azimah Ismail,Hadieh Monajemi,Hafizan Juahir,Ananthy Retnam,Mazlin Mokhtar,Munirah Abdul Zali,Sharifuddin Md Zain,Mohd Ekhwan Toriman,Azlina Md Kassim,Wan Kamaruzaman Wan Ahmad,Saiful Bahri Mohamed,Mohd Zaki Mohd Taib,Siti Nor Fazillah Abdullah

doi:10.2166/wst.2021.038

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.

Highlights

Millions of marine species that are inhabiting the oceans can be endangered if fuel oil is released into the sea or ocean (Gaganis & Pasadakis )
This paper presents Support vector machines (SVM) based on the Kernel – Radial Basis Function (RBF) algorithm, which was applied as a development approach for oil spill type classification using gas chromatography- flame ionization detector (GC-flame ionization detector (FID)) and gas chromatography-mass spectrometry (GC-MS)
We have explored a wide range of applications considering the RBF-SVM model as a decision function classifier to gain high precision of oil type classification performance from the oil spill fingerprints

Summary

INTRODUCTION

Millions of marine species that are inhabiting the oceans can be endangered if fuel oil is released into the sea or ocean (Gaganis & Pasadakis ). The high complexity of the data requires an in-depth study on the restructuring of the data classification into the desired categories that is in line with the global scale in terms of competitiveness (Alamdar et al ) Using innovative methods such as the gas chromatography- flame ionization detector (GC-FID) and gas chromatography-mass spectrometry (GC-MS) enables the oil spill fingerprinting to be used as source identification, oil spill characterization and an environmental forensics technique. The building of substantial non-linear classification boundaries was performed by the kernel function of SVM, as an alternative measure in pattern recognition (Dufrenois & Noyer ) This learning machine enables the improvement of the output results of GC-FID and GC-MS to achieve the best oil type classification and higher profit with no unstable patterns (overfitting).

METHODOLOGY

AND DISCUSSION

CONCLUSION

Findings

CODE AVAILABILITY