A high-resolution wave model is crucial for accurate modeling of sediment and organic material transports, but its computational costs hinder direct coupling to an ecosystem model. We developed a machine learning model using long short-term memory to simulate large-scale, high-resolution waves. Trained with numerical wave model (NWM) outputs and wind data from nine locations, our model successfully replicates NWM results for daily mean significant wave height and period in Chesapeake Bay with identical spatial resolution. Compared to the NWM, the data-driven model has root-mean-square errors below 6 cm for daily mean significant wave height and 1 s for the wave period in the bay. It demonstrates excellent model skills and can accurately forecast daily mean significant wave height and period at NOAA wave stations comparable to NWMs. Using minimal wind data and having a short runtime, our data-driven model shows promise as an alternative for wave forecasting and coupling with sediment and ecological models.

中文翻译：

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切萨皮克湾基于机器学习的高空间分辨率波浪模型

高分辨率波浪模型对于沉积物和有机物质传输的精确建模至关重要，但其计算成本阻碍了与生态系统模型的直接耦合。我们开发了一种机器学习模型，使用长短期记忆来模拟大规模、高分辨率的波浪。经过数值波浪模型 (NWM) 输出和来自九个地点的风数据的训练，我们的模型成功复制了切萨皮克湾每日平均有效波高和周期的 NWM 结果，具有相同的空间分辨率。与 NWM 相比，数据驱动模型的日平均有效波高的均方根误差低于 6 cm，海湾波浪周期的均方根误差低于 1 s。它展示了出色的模型技能，可以准确预测与 NWM 相当的 NOAA 波浪站的日平均有效波高和周期。我们的数据驱动模型使用最少的风数据且运行时间短，有望成为波浪预测以及与沉积物和生态模型耦合的替代方案。