Long-term prediction for vertical bending moment utilizing the AIS data and global wave data

Oka, M.; Ma, Chao

doi:10.1007/s00773-023-00949-2

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…In addition, our model has a wider prediction range. Based on comparative results from references [11][12][13], our research shows a broader prediction range in ship trajectory sequence prediction. Moreover, compared to references [12,14], our model can predict trajectories with longer time steps.…”

Section: Discussionmentioning

confidence: 92%

“…The uncertainty of the marine environment always poses certain challenges to ship trajectory prediction. Unlike cars moving in road areas, which have fixed tracks and trends [11], ships moving in a wide range of sea areas have a certain degree of randomness. This has brought about the current prediction methods of ship trajectories, including traditional mathematical statistical methods and deep learning methods, such as the LSTM (long short-term memory) and transformer models.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Large-Scale Long-Term Prediction of Ship AIS Tracks via Linear Networks with a Look-Back Window Decomposition Scheme of Time Features

Zhao,

Wang,

Gao

et al. 2023

JMSE

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Approximating the positions of vessels near underwater devices, such as unmanned underwater vehicles and autonomous underwater vehicles, is crucial for many underwater operations. However, long-term monitoring of vessel trajectories is challenging due to limitations in underwater communications, posing challenges for the execution of underwater exploration missions. Therefore, trajectory prediction based on AIS data is vital in the fusion of underwater detection information. However, traditional models for underwater vessel trajectory prediction typically work well for only small-scale and short-term predictions. In this paper, a novel deep learning method is proposed that leverages a look-back window to decompose the temporal and motion features of ship movement trajectories, enabling long-term vessel prediction in broader sea areas. This research introduces an innovative model structure that enables trajectory features to be simultaneously learned for a larger range of vessels and facilitates long-term prediction. Through this innovative model design, the proposed model can more accurately predict vessel trajectories, providing reliable and comprehensive forecasting results. Our proposed model outperforms the Nlinear model by a 16% improvement in short-term prediction accuracy and an approximately 8% improvement in long-term prediction accuracy. The model also outperforms the Patch model by 5% in accuracy. In summary, the proposed method can produce competitive predictions for the long-term future trajectory trends of ships in large-scale sea areas.

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Section: Discussionmentioning

confidence: 92%

Section: Related Workmentioning

confidence: 99%

Large-Scale Long-Term Prediction of Ship AIS Tracks via Linear Networks with a Look-Back Window Decomposition Scheme of Time Features

Zhao,

Wang,

Gao

et al. 2023

JMSE

View full text Add to dashboard Cite

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Estimation of the Long-term Maximum Expected Value of Vertical Bending Moment taking the Shape of Directional Wave Spectra into account

Oka,

Ma,

Chen

2023

J.JASNAOE

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The two-dimensional wave spectrum obtained by the hindcast are fitted on the function of the Ochi-Hubble spectrum and Mitsuyasu directional distribution namely 4-parameter spectrum and analyzes the trend of the spectrum shape in the actual sea area in the North Atlantic. The effect on the vertical bending moment(VBM) of a container ship are evaluated. A method to get the long-term maximum expected value utilizing the shape parameters of spectrums is proposed.The occurrence probability of multi-peak spectra tends to be low in sea conditions with high significant wave heights in which occur the maximum wave load. The long-term maximum expected value by using the 4-parameter spectrum was estimated to be larger than the value by using 2-parameter Pierson-Moskowitz spectrum, and contributed to approaching the true value gained directly using the hindcast wave spectrum. A method for efficiently estimating the long-term maximum expected value by using the shape parameters(λ, s) was presented. There is a higher possibility of estimating a larger load when using the realistic shape parameters than when using the IACS2023 spectrum.

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Long-term prediction for vertical bending moment utilizing the AIS data and global wave data

Cited by 2 publications

References 13 publications

Large-Scale Long-Term Prediction of Ship AIS Tracks via Linear Networks with a Look-Back Window Decomposition Scheme of Time Features

Large-Scale Long-Term Prediction of Ship AIS Tracks via Linear Networks with a Look-Back Window Decomposition Scheme of Time Features

Estimation of the Long-term Maximum Expected Value of Vertical Bending Moment taking the Shape of Directional Wave Spectra into account

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