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<p>Waves are important physical phenomena in an ocean, and their accurate prediction is essential for ocean engineering, maritime traffic, and marine early warning systems. This study focuses on the Qinhuangdao Sea area located in the Bohai Sea, China. Herein, we use on-site wind data to correct the reanalysis wind data obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF), improving the accuracy of boundary conditions. Then, we use the Simulating WAves Nearshore (SWAN) model to simulate the regional wave field over time. A regional wave-parameter prediction model is then developed using a limited number of sampled data (covering only 2 years, 2020–2021); the model is based on the Whale Optimization Algorithm (WOA), convolutional neural networks (CNNs), and long short-term memory (LSTM) neural networks. WOA is used to optimize the CNN and LSTM framework; in this framework, CNN extracts spatial features, and the LSTM network captures temporal features, enabling accurate short and long-term predictions of wave height, period, and direction. The experimental results showed that despite the small sample size, the model achieves a goodness of fit of 0.9957 for wave height prediction, 0.9973 for period, and 0.9749 for wave direction in short-term forecasting. As the prediction step size increases, the accuracy of the model decreases. When the prediction step size reaches 9 h, the root mean square error for the prediction of wave height, period, and direction increases to 0.2060 m, 0.4582 s, and 32.5358°, respectively. The reliability and applicability of the model are further validated by the experimental results. Our findings highlighted the potential of the developed model in operational wave forecasting, even with a limited number of sampled data.</p>

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Prediction of Regional Surface Wave Parameters in the Qinhuangdao Sea Using a Deep Learning Model with Limited Observational Data

  • 期刊论文

  • 发表日期：

    2026年1月23日

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  • Lei Wang,
  • Kezhao Fang,
  • Long Zhou,
  • Lixin Gong,
  • Yongwei Huo

摘要

Waves are important physical phenomena in an ocean, and their accurate prediction is essential for ocean engineering, maritime traffic, and marine early warning systems. This study focuses on the Qinhuangdao Sea area located in the Bohai Sea, China. Herein, we use on-site wind data to correct the reanalysis wind data obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF), improving the accuracy of boundary conditions. Then, we use the Simulating WAves Nearshore (SWAN) model to simulate the regional wave field over time. A regional wave-parameter prediction model is then developed using a limited number of sampled data (covering only 2 years, 2020–2021); the model is based on the Whale Optimization Algorithm (WOA), convolutional neural networks (CNNs), and long short-term memory (LSTM) neural networks. WOA is used to optimize the CNN and LSTM framework; in this framework, CNN extracts spatial features, and the LSTM network captures temporal features, enabling accurate short and long-term predictions of wave height, period, and direction. The experimental results showed that despite the small sample size, the model achieves a goodness of fit of 0.9957 for wave height prediction, 0.9973 for period, and 0.9749 for wave direction in short-term forecasting. As the prediction step size increases, the accuracy of the model decreases. When the prediction step size reaches 9 h, the root mean square error for the prediction of wave height, period, and direction increases to 0.2060 m, 0.4582 s, and 32.5358°, respectively. The reliability and applicability of the model are further validated by the experimental results. Our findings highlighted the potential of the developed model in operational wave forecasting, even with a limited number of sampled data.