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http://dx.doi.org/10.15207/JKCS.2020.11.3.053

Oil Spill Visualization and Particle Matching Algorithm  

Lee, Hyeon-Chang (Dept. Computer Science, Kwangwoon University)
Kim, Yong-Hyuk (Dept. Computer Science, Kwangwoon University)
Publication Information
Journal of the Korea Convergence Society / v.11, no.3, 2020 , pp. 53-59 More about this Journal
Abstract
Initial response is important in marine oil spills, such as the Hebei Spirit oil spill, but it is very difficult to predict the movement of oil out of the ocean, where there are many variables. In order to solve this problem, the forecasting of oil spill has been carried out by expanding the particle prediction, which is an existing study that studies the movement of floats on the sea using the data of the float. In the ocean data format HDF5, the current and wind velocity data at a specific location were extracted using bilinear interpolation, and then the movement of numerous points was predicted by particles and the results were visualized using polygons and heat maps. In addition, we propose a spill oil particle matching algorithm to compensate for the lack of data and the difference between the spilled oil and movement. The spilled oil particle matching algorithm is an algorithm that tracks the movement of particles by granulating the appearance of surface oil spilled oil. The problem was segmented using principal component analysis and matched using genetic algorithm to the point where the variance of travel distance of effluent oil is minimized. As a result of verifying the effluent oil visualization data, it was confirmed that the particle matching algorithm using principal component analysis and genetic algorithm showed the best performance, and the mean data error was 3.2%.
Keywords
Oil spill; Data Visualization; Genetic Algorithm; Principal Component Analysis; Machine Learning;
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Times Cited By KSCI : 4  (Citation Analysis)
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