ORIGINAL RESEARCH article
Front. Mar. Sci.
Sec. Marine Affairs and Policy
Volume 12 - 2025 | doi: 10.3389/fmars.2025.1547933
This article is part of the Research TopicEmerging Computational Intelligence Techniques to Address Challenges in Oceanic ComputingView all 7 articles
A multi-dimensional data-driven ship roll prediction model based on VMD-PCA and IDBO-TCN-BiGRU-Attention
Provisionally accepted
- Guangdong Ocean University, Zhanjiang, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
The motion of a ship at sea is complex. Its motion is affected by environmental factors like wind, waves, and currents. These factors cause the ship's movement to be nonlinear, dynamic, and uncertain. Such complex motion can impact the ship ' s performance and pose a safety risk, especially during take-off and landing operations of ship and vessel aircraft. This has become an urgent problem in maritime safety.This study aims to improve the prediction of a ship's roll motion with high accuracy.To do so, the study proposes a combined prediction model. This model integrates data decomposition, dimensionality reduction, deep learning, and optimization techniques. First, the model uses the variational mode decomposition (VMD) method to break down the ship roll motion data into components at different scales. This improves the smoothness of the data. Second, principal component analysis (PCA) is applied to reduce the dimensionality of the decomposed components. This step helps remove noise and redundant features that could affect the prediction results. The core of the model combines Temporal Convolutional Networks (TCN) and Bi-directional Gated Recurrent Units (BiGRU). These deep learning techniques allow the model to extract both spatial features and temporal dependencies from the data. An attention mechanism is added to focus on the most important features, improving the model's prediction accuracy. Finally, the Improved Dung Beetle Optimization (IDBO) algorithm is used to optimize the model ' s hyperparameters. This step further enhances the model's performance. To test the model, simulation experiments were conducted using full-scale data from the Yukun ship. The results show that the proposed model outperforms traditional methods in terms of accuracy and stability.This demonstrates its potential for improving the prediction of ship motion and attitude.
Keywords: Ship rolling motion, Multi-Dimensional Data-Driven, Principal Component Analysis, Variational mode decomposition, Temporal Convolutional Network, Bidirectional gated recurrent unit, Improved Dung Beetle Optimization
Received: 28 Feb 2025; Accepted: 13 May 2025.
Copyright: © 2025 Wang, Yin, Wang and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Jianchuan Yin, Guangdong Ocean University, Zhanjiang, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.