论文标题
使用深度学习学习风与明显的波高度之间的时空关系
Learning the spatio-temporal relationship between wind and significant wave height using deep learning
论文作者
论文摘要
海浪气候对近岸和离岸人类活动有重大影响，其特征可以帮助设计海洋结构，例如波能转化器和海堤。因此，工程师需要长时间的海浪参数。数值模型是海浪数据的宝贵来源。但是，它们在计算上很昂贵。因此，近几十年来，统计和数据驱动的方法越来越兴趣。这项工作使用两个阶段的深度学习模型调查了比斯开湾北大西洋风与显着波高（HS）之间的时空关系。第一步使用卷积神经网络（CNN）提取有助于HS的空间特征。然后，长期的短期记忆（LSTM）用于学习风与波之间的长期时间依赖性。
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.
