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Please use this identifier to cite or link to this item: http://repository.li.mahidol.ac.th/dspace/handle/123456789/51201
Title: Application of a deep learning technique to the problem of oil spreading in the Gulf of Thailand
Authors: Polapat Khlongkhoi
Kittisak Chayantrakom
Wattana Kanbua
South Carolina Commission on Higher Education
Mahidol University
Marine Meteorological Center
Keywords: Mathematics
Issue Date: 1-Dec-2019
Citation: Advances in Difference Equations. Vol.2019, No.1 (2019)
Abstract: © 2019, The Author(s). One of the important mechanisms of the oil weathering processes (OWP) is spreading of oil spills. This mechanism is the horizontal expansion of the oil slick with inertia-gravity, gravity-viscosity, and viscous-surface tension. In the prediction of spreading, the surface of the slick can be considered as an ellipse where the major axis is in the direction of the wind. Ocean wave models, which account for the interaction between wind and waves, can be used to predict the state of the sea including wind direction in two dimensions where the wave spectrum is allowed to evolve freely with no constraints on the spectral shape. However, the wave model simulation for long duration is time-consuming. In this study, the technique of deep learning, a part of the machine learning method, is implemented to obtain a model used to get quick prediction of the wind direction. The technique uses outputs from an ocean wave model and applies the multivariate time series to obtain a linear relationship among multiple time series of wind prediction from the wave model. The wind forecast is taken as inputs to the deep learning model. Some of these inputs that are significant are selected by using the sigmoid function which is an activation function. The minimum error of prediction from the deep learning model is obtained by the gradient descent method. The numerical results of the prediction spreading of oil spill in the Gulf of Thailand based on the wind prediction by the deep learning technique are presented.
URI: http://repository.li.mahidol.ac.th/dspace/handle/123456789/51201
metadata.dc.identifier.url: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85069641859&origin=inward
ISSN: 16871847
16871839
Appears in Collections:Scopus 2019

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