Ocean wave climate has a significant impact on near-shore and off-shore human activities, and its characterisation 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 spatio-temporal 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 (CNNs) to extract the spatial features that contribute to Hs. Then, long short-term memory (LSTM) is used to learn the long-term temporal dependencies between wind and waves.
翻译:海洋波浪气候对近岸和近海人类活动有重大影响,其特征化有助于设计海洋结构,如波能转换器和海上潜水器。因此,工程师需要长期的海洋波参数序列。数字模型是海洋波数据的宝贵来源,但计算成本很高。因此,近几十年来,统计和数据驱动方法越来越受关注。这项工作利用两阶段深学习模型,调查北大西洋风与比斯卡湾岸外地点高浪(Hs)之间的时空关系。第一步利用进化神经网络(CNNs)提取有助于Hs的空间特征。随后,长期短期记忆(LSTM)用于了解风与波之间的长期时间依赖性。