Wave Prediction in the Sea of Japan by Deep Learning Using Meteorological Data-Reference-Cited by-同舟云学术

Wave Prediction in the Sea of Japan by Deep Learning Using Meteorological Data

Published:2019 Issue:2 Volume:75 Page:I_145-I_150
ISSN:1883-8944
Container-title:Journal of Japan Society of Civil Engineers, Ser. B2 (Coastal Engineering)
language:en
Short-container-title:Journal of Japan Society of Civil Engineers, Ser. B2 (Coastal Engineering)

Author:

Tracey H. A. TOM ¹,IKEMOTO Ai²,MASE Hajime³,KAWASAKI Koji⁴,TAKEDA Masahide⁵,KIM Sooyoul⁶

Affiliation:

1. Hydro Technology Institute Co., Ltd.

2. HOKUULA Co. Ltd.

3. Disaster Prevention Research Institute, Kyoto University, Research Advisor Hydro Technology Institute Co., Ltd.

4. Research and Development Center, Hydro Technology Institute Co., Ltd.

5. Research and Development Center, TOA Corpotation

6. Social Systems and Civil Engineering, Tottori University

Publisher

Japan Society of Civil Engineers

Link

https://www.jstage.jst.go.jp/article/kaigan/75/2/75_I_145/_pdf

Reference7 articles.

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2. 2) Hornik, K., Stinchcombe, M., White, H. : Multilayer Feedforward Networks are Universal Approximators, Neural Networks, Vol. 2, pp. 359-366, 1989.

3. 3) Szegedy, C., Vanhoucke, V., Ioffe, S, Shlens, J., and Wojna, Z. : Rethinking the inception architecture for computer vision, arXiv preprint arXiv:1512.00567, 2015.

4. 4) Chollet, F. : Xception Deep Learning with Depthwise Separable Convolutions, arXiv preprint arXiv:1610.02357, 2016.

5. 5) RISH: Research Institute for Sustainable Humanosphere, Grid Point Value Meso-Scale Model (GPV-MSM) data, http://datbase.rish.kyoto-u.ac.jp/arch/jmadata/data/gpv/netcdf/MSM-S, 2019-03-13.

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