FN Archimer Export Format
PT J
TI Learning the spatiotemporal relationship between wind and significant wave height using deep learning
BT 
AF OBAKRIM, Said
   MONBET, Valerie
   RAILLARD, Nicolas
   AILLIOT, Pierre
AS 1:1,2;2:1;3:2;4:3;
FF 1:;2:;3:PDG-REM-RDT-LHYMAR;4:;
C1 Univ Rennes, CNRS, IRMAR – UMR 6625, Rennes, France
   IFREMER, RDT, F-29280 Plouzané, France
   Laboratoire de Mathématiques de Bretagne Atlantique, Université de Brest, Brest, France
C2 UNIV RENNES, FRANCE
   IFREMER, FRANCE
   UBO, FRANCE
SI BREST
SE PDG-REM-RDT-LHYMAR
TC 0
UR https://archimer.ifremer.fr/doc/00830/94219/101601.pdf
   https://archimer.ifremer.fr/doc/00830/94219/105679.pdf
LA English
DT Article
DE ;Convolutional neural networks;long short-term memory;significant wave height;wind fields
AB Ocean wave climate has a significant impact on near-shore and off-shore human activities, and its characterization can help in the design of ocean structures such as wave energy converters and sea dikes. Therefore, engineers need long time series of ocean wave parameters. Numerical models are a valuable source of ocean wave data; however, they are computationally expensive. Consequently, statistical and data-driven approaches have gained increasing interest in recent decades. This work investigates the spatiotemporal relationship between North Atlantic wind and significant wave height (Hs) at an off-shore location in the Bay of Biscay, using a two-stage deep learning model. The first step uses convolutional neural networks to extract the spatial features that contribute to Hs. Then, long short-term memory is used to learn the long-term temporal dependencies between wind and waves. 
PY 2023
PD FEB
SO Environmental Data Science
SN 2634-4602
PU Cambridge University Press
VL 2
IS E5
DI 10.1017/eds.2022.35
ID 94219
ER

EF