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- 저자 : Tracey H.A. Tom (검색결과 1 건)
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Tracey H.A. Tom,Hajime Mase,Makoto Hasegawa,정재훈(Jae-Hoon Jeong),윤종성(Jong-Sung Yoon),김연중(Yeon-Joong Kim) 한국연안방재학회 2020 한국연안방재학회지 Vol.7 No.4
Numerical wave prediction models require a large amount of computational power to timely complete the required calculations. Artificial Neural Networks (ANN) have been introduced to perform predictions at a lesser computational cost and increased processing speed. Deep learning and specifically Convolutional Neural Networks (CNN) have become accepted for various image recognition applications. Motivation for the examination of wave prediction by deep learning came from the success of CNN in vision applications and the similarity of meteorological weather grid data to visual images. This study investigates a deep learning technique using the Japan Meteorological Agency’s Grid Point Value Mesoscale Model to predict wave height and period. In particular, this study uses the Xception deep learning architecture with depthwise separable convolution to obtain improved wave height and period prediction over artificial neural networks, and gets overall success results.
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