Support vector machines for oil classification link with polyaromatic hydrocarbon contamination in the environment
| Autor: | Saiful Bahri Mohamed, Mohd. Zaki Mohd. Taib, Munirah Abdul Zali, Mohd Ekhwan Toriman, Siti Nor Fazillah Abdullah, Hafizan Juahir, Hadieh Monajemi, Azimah Ismail, Azlina Md. Kassim, Wan Kamaruzaman Wan Ahmad, Ananthy Retnam, Sharifuddin M. Zain, Mazlin Mokhtar |
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| Rok vydání: | 2021 |
| Předmět: |
Support Vector Machine
Environmental Engineering 02 engineering and technology 010501 environmental sciences Environmental technology. Sanitary engineering 01 natural sciences support vector machines Diesel fuel oil classification 0202 electrical engineering electronic engineering information engineering Petroleum Pollution Polycyclic Aromatic Hydrocarbons classifier TD1-1066 0105 earth and related environmental sciences Water Science and Technology chemistry.chemical_classification Petroleum engineering Malaysia Waste oil fingerprints Fuel oil Hydrocarbons Slack variable Support vector machine Hydrocarbon chemistry Decision boundary Environmental science 020201 artificial intelligence & image processing Subspace topology |
| Zdroj: | Water Science and Technology, Vol 83, Iss 5, Pp 1039-1054 (2021) |
| ISSN: | 1996-9732 0273-1223 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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