期限(时间)
航程(航空)
计算机科学
有效波高
过程(计算)
预测技巧
系列(地层学)
波高
操作员(生物学)
气象学
风浪
工程类
地理
地质学
古生物学
生物化学
海洋学
物理
化学
抑制因子
量子力学
转录因子
基因
航空航天工程
操作系统
作者
Yuval Yevnin,Shir Chorev,Ilan Dukan,Yaron Toledo
标识
DOI:10.1016/j.oceaneng.2022.113389
摘要
Short-term ocean waves forecasting requires a high degree of skill and knowledge, as one should observe the available model forecast and real-time measurement and reach a combined estimation. This paper presents a deep learning model providing a short-term wave height prediction derived from recent in-situ measurements and an available mid-range forecast. The model is based of a gated recurrent unit, which is common in time-series forecasting. The model is able to improve significant wave height RMSE by as much as 76% for 1 h forecasts and converge to ∼12% improvement for forecasts over 7 h. The model is also shown to be easily transferable to another station and achieves good performance without further training in a ”zero-shot” learning process. This model can prove valuable to various off-shore operations, allowing for data-driven decision making instead of skilled human operator and experience-based evaluation.
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