딥러닝을 이용한 장기 파랑예측 가능성 연구

• 영문명: Possibility of Wave Prediction by Deep Learning
• 발행기관: 한국연안방재학회
• 저자명: Tracey H.A. Tom Hajime Mase Makoto Hasegawa 정재훈(Jae-Hoon Jeong) 윤종성(Jong-Sung Yoon) 김연중(Yeon-Joong Kim)
• 간행물 정보: 『한국연안방재학회지』제7권 제4호, 195~201쪽, 전체 7쪽
• 주제분류: 사회과학 > 사회과학일반
• 파일형태: PDF
• 발행일자: 2020.10.30

영문 초록

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.

목차

1. 서 론

2. Xception을 이용한 딥러닝

3. 데이터 세트와 검토방법

4. 결과 및 논의

5. 결 론

References

키워드

Deep learning Convolutional neural network Wave prediction Xception