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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: 25 September 2025 | Viewed by 641

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 100 words) can be sent to the Editorial Office for announcement on this website.

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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Published Papers (1 paper)

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Research

49 pages, 5229 KiB  
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 407
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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