East Coast Environmental Research Institute (ESERI), Universiti Sultan Zainal Abidin, Gong Badak Campus, 21300 Kuala Terengganu, Malaysia E-mail:
Faculty of Design Innovative and Technology(FRIT), Universiti Sultan Zainal Abidin, Gong Badak Campus, 21300 Kuala Terengganu, Malaysia.
Water Sci Technol. 2021 Mar;83(5):1039-1054. doi: 10.2166/wst.2021.038.
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.
本研究的主要重点是通过基于核径向基函数（RBF）的支持向量机方法来探索环境中石油溢油中多环芳烃的空间分布关系，以实现对马来西亚半岛油样指纹的高精度油溢油类型分类。结果表明，来自 PP3_liquid 和 GP6_Jetty 油样的 Σ 烷基化 PAHs 和 Σ EPA PAHs 在 ΣTAH 浓度中达到了最高浓度，达到了 100%的分类输出，对应于一致的决策边界和投影子空间估计。这种方法的高维性质导致了油类型分类与四个聚类油类型成分（即柴油、Bunker C、混合油（MO）、润滑油和废油（WO））之间存在完美的可分离性，其中松弛变量 ξ ≠ 0。在这四个聚类中，只有两个 SVs 被正确预测，即柴油和 MO。核 RBF 方法提供了高效可靠的油样分类，使得在相对较短的执行时间内能够最优地执行油分类，并且在松弛变量 ξ 非零的情况下更快地进行数据集分类。
