25 October 2016 Quantitative analysis of multi-component complex oil spills based on the least-squares support vector regression
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Proceedings Volume 10156, Hyperspectral Remote Sensing Applications and Environmental Monitoring and Safety Testing Technology; 1015614 (2016) https://doi.org/10.1117/12.2246674
Event: International Symposium on Optoelectronic Technology and Application 2016, 2016, Beijing, China
Abstract
Quantitative analysis of the simulated complex oil spills was researched based on PSO-LS-SVR method. Forty simulated mixture oil spills samples were made with different concentration proportions of gasoline, diesel and kerosene oil, and their near infrared spectra were collected. The parameters of least squares support vector machine were optimized by particle swarm optimization algorithm. The optimal concentration quantitative models of three-component oil spills were established. The best regularization parameter C and kernel parameter σ of gasoline, diesel and kerosene model were 48.1418 and 0.1067, 53.2820 and 0.1095, 59.1689 and 0.1000 respectively. The decision coefficient R2 of the prediction model were 0.9983, 0.9907 and 0.9942 respectively. RMSEP values were 0.0753, 0.1539 and 0.0789 respectively. For gasoline, diesel fuel and kerosene oil models, the mean value and variance value of predict absolute error were -0.0176±0.0636 μL/mL, -0.0084±0.1941 μL/mL, and 0.00338±0.0726 μL/mL respectively. The results showed that each component’s concentration of the oil spills samples could be detected by the NIR technology combined with PSO-LS-SVR regression method, the predict results were accurate and reliable, thus this method can provide effective means for the quantitative detection and analysis of complex marine oil spills.
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Ailing Tan, Yong Zhao, Siyuan Wang, "Quantitative analysis of multi-component complex oil spills based on the least-squares support vector regression", Proc. SPIE 10156, Hyperspectral Remote Sensing Applications and Environmental Monitoring and Safety Testing Technology, 1015614 (25 October 2016); doi: 10.1117/12.2246674; https://doi.org/10.1117/12.2246674
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