Due to strong non-linearity, ocean surface gravity waves are difficult to directly and accurately predict, despite their importance for a wide range of coastal, nearshore, and offshore activities. To minimize forecast errors, a hybrid combined improved empirical wavelet transform decomposition (IEWT) and long-short term memory network (LSTM) model has been proposed. Data from National Data Buoy Center buoys deployed in the North Pacific Ocean are taken as an example to verify the models. Wave forecasts using the LSTM, EWT-LSTM, and IWET-LSTM models are compared with the observations at 6, 12, 18, 24 and 48 h forecast windows. Consequently, IEWT-LSTM is superior to EWT-LSTM or LSTM models, especially for larger waves at longer long forecast windows.

中文翻译：

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基于改进经验小波变换分解和长短期记忆网络的有效波高预测混合模型

由于强烈的非线性，海洋表面重力波难以直接、准确地预测，尽管它们对于广泛的沿海、近岸和近海活动很重要。为了最大限度地减少预测误差，提出了一种混合组合的改进经验小波变换分解（IEWT）和长短期记忆网络（LSTM）模型。以部署在北太平洋的国家数据浮标中心浮标数据为例对模型进行验证。使用 LSTM、EWT-LSTM 和 IWET-LSTM 模型进行的波浪预测与 6、12、18、24 和 48 小时预测窗口的观测结果进行了比较。因此，IEWT-LSTM 优于 EWT-LSTM 或 LSTM 模型，特别是对于较长预报窗口下的较大波浪。