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Machine Learning for Prediction of Ship Motion
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Machine Learning for Prediction of Ship Motion

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Ocean Engineering".

Deadline for manuscript submissions: 28 February 2026 | Viewed by 4932

Special Issue Editors

Dr. Yangjun Ahn

E-Mail Website
Guest Editor
School of AI Convergence, Sungshin Women’s University, Seoul, Republic of Korea
Interests: machine learning; hydrodynamics; sloshing; artificial intelligence; seakeeping
Prof. Dr. Kwang-Jun Paik

E-Mail Website
Guest Editor
Department of Naval Architecture and Ocean Engineering, Inha University, Incheon, Republic of Korea
Interests: computational fluid dynamics; CFD simulation; mechanical engineering; hydrodynamics; fluid structure interaction; OpenFOAM

Special Issue Information

Dear Colleagues,

Ship motion prediction is crucial in maritime operations, particularly in navigation safety, control, and operational efficiency. Since the advancement of machine learning, researchers have increasingly explored data-driven approaches to enhance the accuracy and reliability of ship motion forecasting. This Special Issue highlights the latest advancements in machine learning techniques for short-term time series prediction of ship motion and system identification methods for estimating hydrodynamic coefficients related to seakeeping and maneuvering.

This Special Issue aims to publish cutting-edge research in these domains, ensuring rapid peer review and dissemination of high-quality studies for research and practical applications.

We welcome high-quality papers directly addressing various aspects of ship motion prediction, including, but not limited to, the following:

  • Machine learning-based short-term time series prediction for ship motion;
  • Data-driven approaches for ship maneuvering and seakeeping analysis;
  • System identification methods for estimating hydrodynamic coefficients;
  • Applications of AI in ship stability, route optimization, and safety enhancement.

We encourage novel methodologies and interdisciplinary research to further the understanding and application of machine learning in ship motion prediction.

Dr. Yangjun Ahn
Prof. Dr. Kwang-Jun Paik
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Marine Science and Engineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • ship motion prediction
  • machine learning
  • artificial intelligence
  • system identification
  • time series prediction
  • seakeeping
  • maneuvering

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

30 pages, 28169 KB  
Article
System Identification of a Moored ASV with Recessed Moon Pool via Deterministic and Bayesian Hankel-DMDc
by Giorgio Palma, Ivan Santic, Andrea Serani, Lorenzo Minno and Matteo Diez
J. Mar. Sci. Eng. 2025, 13(12), 2267; https://doi.org/10.3390/jmse13122267 - 28 Nov 2025
Viewed by 162
Abstract
This study addresses the system identification of a small autonomous surface vehicle (ASV) under moored conditions using Hankel dynamic mode decomposition with control (HDMDc) and its Bayesian extension (BHDMDc). Experiments were carried out on a Codevintec CK-14e ASV in the CNR-INM towing tank, [...] Read more.
This study addresses the system identification of a small autonomous surface vehicle (ASV) under moored conditions using Hankel dynamic mode decomposition with control (HDMDc) and its Bayesian extension (BHDMDc). Experiments were carried out on a Codevintec CK-14e ASV in the CNR-INM towing tank, under both irregular and regular head wave conditions. The ASV under investigation features a recessed moon pool, which induces nonlinear responses due to sloshing, thereby increasing the modeling challenge. Data-driven reduced-order models were built from measurements of vessel motions and mooring loads. The HDMDc framework provided accurate deterministic predictions of vessel dynamics, while the Bayesian formulation enabled uncertainty-aware characterization of the model response by accounting for variability in hyperparameter selection. Validation against experimental data demonstrated that both HDMDc and BHDMDc can predict the vessel’s response under unseen regular and irregular wave excitations. In conclusion, this study shows that HDMDc-based ROMs are a viable data-driven alternative for system identification, demonstrating for the first time their generalization capability for an unseen sea condition different from the training set, achieving high accuracy in reproducing the vessel dynamics. Full article
(This article belongs to the Special Issue Machine Learning for Prediction of Ship Motion)
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30 pages, 12036 KB  
Article
Comparative Studies of Physics- and Machine Learning-Based Wave Buoy Analogy Models Under Various Ship Operating Conditions
by Jae-Hoon Lee, Donghyeong Ko and Ju-Hyuck Choi
J. Mar. Sci. Eng. 2025, 13(9), 1823; https://doi.org/10.3390/jmse13091823 - 20 Sep 2025
Viewed by 819
Abstract
This study presents a comparative analysis of wave buoy analogy models for sea state estimation. A nonparametric, response amplitude operator-based model is introduced as a physics-based approach, while a convolutional neural network is adopted as a machine learning approach. Using time-domain simulation data [...] Read more.
This study presents a comparative analysis of wave buoy analogy models for sea state estimation. A nonparametric, response amplitude operator-based model is introduced as a physics-based approach, while a convolutional neural network is adopted as a machine learning approach. Using time-domain simulation data of wave-induced ship motions under various operating conditions, the accuracy and reliability of each model’s estimation are evaluated. The sensitivity of the physics-based model to operating conditions is examined, along with optimization strategies such as hyperparameter tuning. In particular, regularization techniques based on bilinear and B-spline surface fitting are applied to the nonparametric model, and the effects of interpolation techniques on model performance are assessed. For the machine learning model, a parametric study is conducted to determine input data types and formats, including time series and spectral representations, as well as the required length of the time window and dataset volume. Finally, the feasibility of the proposed neural network in estimating not only sea state parameters but also loading and navigational information, such as ship speed and GM, is discussed. Full article
(This article belongs to the Special Issue Machine Learning for Prediction of Ship Motion)
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35 pages, 6406 KB  
Article
Comparative Study of RNN-Based Deep Learning Models for Practical 6-DOF Ship Motion Prediction
by HaEun Lee and Yangjun Ahn
J. Mar. Sci. Eng. 2025, 13(9), 1792; https://doi.org/10.3390/jmse13091792 - 17 Sep 2025
Cited by 2 | Viewed by 1040
Abstract
Accurate prediction of ship motion is essential for ensuring the safety and efficiency of maritime operations. However, the ship dynamics’ nonlinear, non-stationary, and environment-dependent nature presents significant challenges for reliable short-term forecasting. This study uses a simulated dataset designed to reflect realistic maritime [...] Read more.
Accurate prediction of ship motion is essential for ensuring the safety and efficiency of maritime operations. However, the ship dynamics’ nonlinear, non-stationary, and environment-dependent nature presents significant challenges for reliable short-term forecasting. This study uses a simulated dataset designed to reflect realistic maritime variability to evaluate the performance of recurrent neural network (RNN)-based models—including RNN, LSTM, GRU, and Bi-LSTM—under both single and multi-environment conditions. The analysis examines the effects of input sequence length, downsampling intervals, model complexity, and input dimensionality. Results show that Bi-LSTM consistently outperforms unidirectional architectures, particularly in complex multi-environment scenarios. In single-environment settings, the prediction horizon exceeded 40 s, while it decreased to around 20 s under more variable conditions, reflecting generalization challenges. Multi-degree-of-freedom (DOF) inputs enhanced performance by capturing the coupled nature of ship dynamics, whereas incorporating wave height data yielded inconsistent results. A sequence length of 200 timesteps and a downsampling interval of 5 effectively balanced motion feature preservation with high-frequency noise reduction. Increasing model size improved accuracy up to 256 hidden units and 10 layers, beyond which performance gains diminished. Additionally, Peak Matching was introduced as a complementary metric to MSE, emphasizing the importance of accurately predicting motion extrema for practical maritime applications. Full article
(This article belongs to the Special Issue Machine Learning for Prediction of Ship Motion)
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18 pages, 1960 KB  
Article
A GRNN Neural Network-Based Surrogate Model for Ship Dynamic Stability Calculation
by Qiang Sun, Jie Tan and Yaohua Zhou
J. Mar. Sci. Eng. 2025, 13(9), 1777; https://doi.org/10.3390/jmse13091777 - 15 Sep 2025
Viewed by 745
Abstract
The assessment of ship dynamic stability in waves is crucial for navigation safety. To mitigate accidents, the International Maritime Organization (IMO) has formulated corresponding technical standards. However, evaluating the dynamic stability performance of ships involves complex numerical simulation or model experiments based on [...] Read more.
The assessment of ship dynamic stability in waves is crucial for navigation safety. To mitigate accidents, the International Maritime Organization (IMO) has formulated corresponding technical standards. However, evaluating the dynamic stability performance of ships involves complex numerical simulation or model experiments based on hydrodynamic methods, which demands professionalism, substantial time, and significant financial cost. This paper analyzes the feasibility of using the Generalized Regression Neural Network (GRNN) method to build a surrogate model for ship dynamic stability performance calculation. Comparisons with hydrodynamics-based simulations reveal that the surrogate model matches the trends well, yet the root-mean-square error (RMSE) remains non-negligible. Therefore, an improved GRNN surrogate model is proposed to solve this problem. By incorporating enhanced feature preprocessing and clustering techniques, the improved model not only increases predictive accuracy but also achieves significant efficiency gains, reducing the computational time from days or weeks for numerical simulations to seconds or minutes. Experimental results show that the improved surrogate model outperforms the baseline GRNN model, and this framework can serve as a practical surrogate for hydrodynamics-based numerical models to rapidly assess pre-voyage dynamic stability. Full article
(This article belongs to the Special Issue Machine Learning for Prediction of Ship Motion)
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49 pages, 5229 KB  
Article
Enhancing Ship Propulsion Efficiency Predictions with Integrated Physics and Machine Learning
by Hamid Reza Soltani Motlagh, Seyed Behbood Issa-Zadeh, Md Redzuan Zoolfakar and Claudia Lizette Garay-Rondero
J. Mar. Sci. Eng. 2025, 13(8), 1487; https://doi.org/10.3390/jmse13081487 - 31 Jul 2025
Viewed by 1620
Abstract
This research develops a dual physics-based machine learning system to forecast fuel consumption and CO2 emissions for a 100 m oil tanker across six operational scenarios: Original, Paint, Advanced Propeller, Fin, Bulbous Bow, and Combined. The combination of hydrodynamic calculations with Monte [...] Read more.
This research develops a dual physics-based machine learning system to forecast fuel consumption and CO2 emissions for a 100 m oil tanker across six operational scenarios: Original, Paint, Advanced Propeller, Fin, Bulbous Bow, and Combined. The combination of hydrodynamic calculations with Monte Carlo simulations provides a solid foundation for training machine learning models, particularly in cases where dataset restrictions are present. The XGBoost model demonstrated superior performance compared to Support Vector Regression, Gaussian Process Regression, Random Forest, and Shallow Neural Network models, achieving near-zero prediction errors that closely matched physics-based calculations. The physics-based analysis demonstrated that the Combined scenario, which combines hull coatings with bulbous bow modifications, produced the largest fuel consumption reduction (5.37% at 15 knots), followed by the Advanced Propeller scenario. The results demonstrate that user inputs (e.g., engine power: 870 kW, speed: 12.7 knots) match the Advanced Propeller scenario, followed by Paint, which indicates that advanced propellers or hull coatings would optimize efficiency. The obtained insights help ship operators modify their operational parameters and designers select essential modifications for sustainable operations. The model maintains its strength at low speeds, where fuel consumption is minimal, making it applicable to other oil tankers. The hybrid approach provides a new tool for maritime efficiency analysis, yielding interpretable results that support International Maritime Organization objectives, despite starting with a limited dataset. The model requires additional research to enhance its predictive accuracy using larger datasets and real-time data collection, which will aid in achieving global environmental stewardship. Full article
(This article belongs to the Special Issue Machine Learning for Prediction of Ship Motion)
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