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Online prediction method of ship maneuvering motion under wave influence based on improved LSTM neural network
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Lijia Chen1, 2, 3, *, Xinwei Zhou1, 2, 3, Kezhong Liu1, 2, 3, Naifeng Zhang1, 2, 3
Navigation of China | 2026, 49(2) : 15 - 24
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Navigation of China | 2026, 49(2): 15-24
• Marine Traffic Safety •
Online prediction method of ship maneuvering motion under wave influence based on improved LSTM neural network
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Lijia Chen1, 2, 3, *, Xinwei Zhou1, 2, 3, Kezhong Liu1, 2, 3, Naifeng Zhang1, 2, 3
Affiliations
  • 1.School of Navigation, Wuhan University of Technology, Wuhan 430063, China
  • 2.Hubei Key Laboratory of Inland Shipping Technology, Wuhan University of Technology, Wuhan 430063, China
  • 3.State Key Laboratory of Maritime Technology and Safety, Wuhan University of Technology, Wuhan 430063, China
Published: 2026-04-25 doi: 10.3969/j.issn.1000-4653.2026.02.002
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To address the insufficient real-time capability and long-horizon accuracy degradation of ship maneuvering motion prediction under environmental disturbances such as waves, an online prediction method based on an improved Long Short-Term Memory (LSTM) neural network is proposed. A multi-layer LSTM is adopted as the core predictor, and an embedded sliding-window structure is introduced to compute the error metrics within the window in real time. When the window-averaged error exceeds a preset threshold, model retraining and updating are triggered, thereby achieving timely online prediction. The results indicate that, compared with offline prediction, the proposed online method maintains stable prediction accuracy under long-horizon conditions with continuously switching wave states. With the same window length, the online method with a stricter threshold achieves a maximum RMSE improvement of 56.85%, while the cumulative update time is only 3.82 s. The proposed online prediction method delivers satisfactory long-horizon prediction performance for ship maneuvering motion and shows practical value for accurate long-horizon prediction under complex sea conditions. Key words:navigation safety; online prediction; long short-term memory neural network; ship maneuvering; wave influence; sliding time window

Lijia Chen, Xinwei Zhou, Kezhong Liu, Naifeng Zhang. Online prediction method of ship maneuvering motion under wave influence based on improved LSTM neural network[J]. Navigation of China, 2026 , 49 (2) : 15 -24 . DOI: 10.3969/j.issn.1000-4653.2026.02.002
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Year 2026 volume 49 Issue 2
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Article Info
doi: 10.3969/j.issn.1000-4653.2026.02.002
  • Receive Date：2025-01-25
  • Online Date：2026-05-19
  • Published：2026-04-25
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  • Received：2025-01-25
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Affiliations
    1.School of Navigation, Wuhan University of Technology, Wuhan 430063, China
    2.Hubei Key Laboratory of Inland Shipping Technology, Wuhan University of Technology, Wuhan 430063, China
    3.State Key Laboratory of Maritime Technology and Safety, Wuhan University of Technology, Wuhan 430063, China
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https://castjournals.cast.org.cn/joweb/zghh/EN/10.3969/j.issn.1000-4653.2026.02.002
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表12种不同金属材料的力学参数
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小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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