Application of a deep learning technique to the problem of oil spreading in the Gulf of Thailand

Advances in Difference Equations

Polapat Khlongkhoi¹  Kittisak Chayantrakom¹  Wattana Kanbua² 

[1] Department of Mathematics, Faculty of Science, Mahidol University;Thai Marine Meteorological Center, Thai Meteorological Department;

关键词: Deep learning;    Gradient descent;    Ocean wave model;    Oil spill;    Sigmoid function;    Multivariate time series;   

DOI  :  10.1186/s13662-019-2241-y

来源: DOAJ

【 摘 要 】

Abstract 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.

【 授权许可】

Unknown