船舶作为重要的运输工具，其安全性和稳定性受到了广泛关注。在恶劣海况下的横摇运动不仅影响货物的运输效率，更直接威胁到船员的生命安全和船舶的完整性。目前大多数预测船舶横摇运动的方法在实际应用中仍存在一定局限性，尤其在预测精度、计算效率、复杂多变的海洋环境的适应性等方面。本文提出基于深度学习的CEEMDAN-WOA-LSTM混合模型，通过先进的数据处理技术和优化算法，对不同船型的横摇运动数据进行训练建模和仿真预报，结果表明，本文提出的混合模型和优化方法，提高了船舶横摇运动预报的准确性和效率以及适应性，为海上交通安全管理提供强有力的技术支持。
As an important means of transportation, ships have received widespread attention for their safety and stability. The rolling motion in adverse sea conditions not only affects the transportation efficiency of goods, but also directly threatens the safety of crew members and the integrity of the ship. At present, most methods for predicting ship roll motion still have certain limitations in practical applications, especially in terms of prediction accuracy, computational efficiency, and adaptability to complex and changing marine environments. This paper proposes a deep learning based CEEMDAN-WOA-LSTM hybrid model, which uses advanced data processing techniques and optimization algorithms to train, model, and simulate the roll motion data of different ship types. The results show that the proposed hybrid model and optimization method improve the accuracy, efficiency, and adaptability of ship roll motion prediction, providing strong technical support for maritime traffic safety management.
2025,47(12): 6-13 收稿日期：2024-8-27
DOI：10.3404/j.issn.1672-7649.2025.12.002
分类号：U661
基金项目：国家重点研发计划资助项目（2022YFB4301402）
作者简介：宋新宇(1998-),男,硕士研究生,研究方向为船舶耐波性运动预报
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