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A training strategy to improve the generalization capability of deep learning-based significant wave height prediction models in offshore China

文献类型： 外文期刊

作者： Huang, Wenchao 1 ; Zhao, Xinying 1 ; Huang, Wenyun 1 ; Hao, Wei 3 ; Liu, Yuliang 3 ;

作者机构： 1.Chinese Acad Fishery Sci, Fishery Machinery & Instrument Res Inst, Shanghai 200092, Peoples R China

2.Pilot Natl Lab Marine Sci & Technol Qingdao, Joint Lab Deep Blue Fishery Engn, Qingdao 266237, Peoples R China

3.Harbin Engn Univ, Qingdao Innovat & Dev Base, Qingdao 266000, Peoples R China

4.Harbin Engn Univ, Coll Shipbldg Engn, Harbin 150001, Peoples R China

关键词： Significant wave height forecast; Deep learning model; Training strategy; Model generalization

期刊名称：OCEAN ENGINEERING （ 影响因子：5.0； 五年影响因子：5.2 ）

ISSN： 0029-8018

年卷期： 2023 年 283 卷

页码：

收录情况： SCI

摘要： Accurate forecasting of significant wave height (SWH) is crucial for ensuring the safety of marine navigation. To achieve good accuracy, deep learning models need to be trained separately using SWH data from all forecast locations. However, improving the generalizability of SWH prediction models is vital for practical engineering purposes. This paper proposes a training strategy that utilizes multi-point data fusion by using wave data ob-tained from different locations to construct a training dataset. To verify the feasibility of training strategy, the artificial neural network (ANN), long short-term memory (LSTM), and temporal convolutional network (TCN) models are used to test in China offshore. The RMSE and MAPE of different models with 2h advance forecasts are almost 0.05 and 4%, indicating that the models have good forecasting performance. Additionally, the models exhibit consistency with the model trained solely by single position data, which demonstrates that the training strategy applies to various models. In addition, in sea areas without wave training data, two locations are randomly selected and used in the pre-trained model to predict SWH. The various models demonstrate favorable forecasting results, further demonstrating that the training strategy proposed in this study can enhance the generalizability of models.

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