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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: closed (25 February 2026) | Viewed by 17847

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Special Issue Editors

Dr. Huanhuan Li

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School of Engineering, University of Southampton, Southampton, UK
Interests: maritime big data mining; maritime safety and security; AI-driven autonomous shipping; decarbonization; green shipping
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Dr. Lu Zou

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Guest Editor

School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, China
Interests: navigation safety assessment of ships under constrained conditions; uncertainty quantification analysis; applications of machine learning in ship hydrodynamics; motion prediction
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Prof. Dr. Sheng Xu

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Guest Editor

School of Naval Architecture and Ocean Engineering, Jiangsu University of Science and Technology, Zhenjiang 212003, China
Interests: offshore renewable energy; mooring analysis; hydrodynamics; structural reliability
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Dr. Zifei Xu

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Guest Editor

Department of Civil and Environmental Engineering, University of Liverpool, Liverpool L69 7ZX, UK
Interests: fault diagnostics; prognostics; intelligent solutions; reliability and safety technologies for mechanical systems
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Special Issue Information

Dear Colleagues,

The increasing availability of marine data, driven by advancements in sensing technologies, automated monitoring, and digitalisation, has created new opportunities and challenges in maritime research. This Special Issue focuses on innovative methodologies and applications of marine data analysis, leveraging advanced artificial intelligence (AI), machine learning, deep learning, and big data analytics to enhance maritime operations, safety, and environmental sustainability.

This Special Issue welcomes research contributions that address key challenges in marine data analysis, including ship trajectory prediction, maritime situational awareness, cybersecurity risk assessment, energy-efficient route optimisation, and autonomous shipping technologies. Studies applying machine learning, deep learning, Bayesian networks (BNs), graph-based modelling, and hybrid AI approaches to extract valuable insights from automatic identification system (AIS) data and/or radar data, satellite observations, wind turbine data, and sensor networks are particularly encouraged. Additionally, research on predictive analytics for maritime safety, emission modelling for sustainable shipping, and AI-driven decision support systems will be considered.

By gathering state-of-the-art research in marine data analysis, this Special Issue aims to advance data-driven decision-making in maritime transportation, improve risk mitigation strategies, and promote the integration of AI in intelligent marine systems. We invite original research and review articles that contribute to developing robust, interpretable, and trustworthy AI solutions for complex maritime environments.

Dr. Huanhuan Li
Dr. Lu Zou
Prof. Dr. Sheng Xu
Dr. Zifei Xu
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 semimonthly 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

marine data analytics
autonomous shipping
big data in maritime transportation
maritime cybersecurity
maritime situational awareness
AI-driven decision support systems
intelligent marine systems
risk assessment

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Published Papers (12 papers)

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Research

17 pages, 857 KB

Open AccessArticle

Toward Realistic Ship Fuel Consumption Prediction Under Chronological Validation

by Aleksandar Vorkapić

J. Mar. Sci. Eng. 2026, 14(6), 538; https://doi.org/10.3390/jmse14060538 - 13 Mar 2026

Viewed by 448

Abstract

Accurate prediction of ship propulsion fuel consumption from operational data is important for performance assessment and energy efficiency management. This study examines how temporal structure and validation strategy influence the predictive performance of regression-based fuel consumption models using real operational data from a seagoing vessel. A controlled experimental framework is used to isolate the effects of chronological validation, temporal feature augmentation based on operational inputs, and autoregressive target information. Under strict chronological validation, a baseline regression model achieves R² = 0.788, while temporal feature augmentation improves performance to R² = 0.845 without using past fuel consumption values. An autoregressive configuration yields R² = 0.982, reflecting strong short-term persistence in the fuel consumption signal. Additional experiments show that random data partitioning can inflate reported R² by up to 0.19 compared with chronological evaluation. The results demonstrate that reported predictive accuracy depends strongly on evaluation design and temporal information structure, highlighting the importance of chronological validation for realistic operational prediction. Full article

(This article belongs to the Special Issue Advanced Studies in Marine Data Analysis)

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13 pages, 2289 KB

Open AccessArticle

Balancing Accuracy and Speed: Improved D-FINE for Real-Time Ocean Internal Wave Detection

by Lu Yu, Yanping Tian, Jie Chen, Cheng Chi, Tingting Li and Jianwei Li

J. Mar. Sci. Eng. 2026, 14(4), 388; https://doi.org/10.3390/jmse14040388 - 19 Feb 2026

Viewed by 420

Abstract

Ocean internal waves (IWs), induced by density stratification and fluid perturbations, are significant oceanic phenomena prevalent across global oceans, profoundly impacting marine environments and engineering safety. Although one-stage object detection models are favored in practical applications due to their efficient inference, they often suffer from insufficient accuracy in IW detection tasks. To address this, we introduce a novel one-stage, anchor-free detection approach based on Transformer for IW detection, proposing a new algorithm named IW-D-FINE, which balances detection accuracy and inference efficiency. On the public SAR dataset, IW-D-FINE achieves an AP@0.5 of 90.5, significantly outperforming existing one-stage methods while maintaining faster inference speeds than mainstream two-stage models. Furthermore, to mitigate the scarcity of internal wave samples, we construct a small-scale IWs dataset, YH3-IW-2025, and validate the algorithm thoroughly on this dataset. Experimental results demonstrate that IW-D-FINE exhibits robust performance under complex background interference, highlighting its application potential and scalability in IW detection tasks. Full article

(This article belongs to the Special Issue Advanced Studies in Marine Data Analysis)

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25 pages, 9604 KB

Open AccessArticle

Shaft-Rate Magnetic Field Localization Algorithm Based on Improved Exponential Triangular Optimization

by Bozhong Lei, Ranfeng Wang, Cheng Chi, Lu Yu, Zhentao Yu and Dan Wang

J. Mar. Sci. Eng. 2026, 14(2), 216; https://doi.org/10.3390/jmse14020216 - 20 Jan 2026

Viewed by 328

Abstract

Addressing the issues of low positioning accuracy and poor robustness in shaft-rate magnetic fields, this study introduces the Improved Exponential Triangular Optimization Algorithm (IETO). By incorporating adaptive attenuation factors, dynamic population reduction, and intelligent boundary contraction strategies, it significantly enhances the global search capability and robustness. A magnetic dipole localization model is developed, and comparative simulations show that IETO achieves reliable accuracy and robustness under low signal-to-noise ratio (SNR) conditions, reducing localization error by 7.82% compared with the conventional Exponential Triangular Optimization Algorithm (ETO). The effects of base station deployment, number of stations, and sea depth on localization performance are further examined, and the capability of IETO for dynamic target tracking is verified. Preliminary sea trial results confirm the practical feasibility and engineering applicability of the proposed method. Full article

(This article belongs to the Special Issue Advanced Studies in Marine Data Analysis)

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26 pages, 3392 KB

Open AccessArticle

From VTS Monitoring to Smart Warnings: Big Data Applications in Channel Safety Management

by Siang-Hua Syue, Ming-Cheng Tsou and Tzu-Hsun Chen

J. Mar. Sci. Eng. 2025, 13(12), 2324; https://doi.org/10.3390/jmse13122324 - 7 Dec 2025

Viewed by 775

Abstract

With the trend of internationalization, maritime traffic density has gradually increased. Since 2002, the International Maritime Organization (IMO) has required various types of vessels to be equipped with the Automatic Identification System (AIS). Through AIS static and dynamic data, more complete navigational information of vessels can be obtained. As the Port of Kaohsiung is currently transitioning into a smart port, this study focuses on inbound and outbound vessels of the Second Port of Kaohsiung. It considers both the safety monitoring of the smart port and environmental security, integrating a big data database to provide early warnings for abnormal navigation conditions. This study builds an integrated database based on vessel AIS data, conducts AIS big data analysis to extract useful information, and establishes a random forest model to predict whether a vessel’s course and speed during port navigation deviate from normal patterns, thereby achieving the goal of early warning. This study also helps reduce the risk of collisions caused by abnormal vessel operations and thus prevents marine pollution in the port area due to oil spills or hazardous substance leakage. Through real-time monitoring and early warning of navigation behavior, it not only enhances navigation safety but also serves as the first line of defense against marine pollution, contributing significantly to the protection of the port’s ecological environment and the promotion of sustainable development. Full article

(This article belongs to the Special Issue Advanced Studies in Marine Data Analysis)

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27 pages, 13030 KB

Open AccessArticle

Hybrid Log-Mel and HPSS-Aided Convolutional Neural Network for Underwater Very-Low-Frequency Remote Passive Sonar Detection

by Haitao Dong, Lijian Yang, Yuan Liu and Siyuan Li

J. Mar. Sci. Eng. 2025, 13(11), 2030; https://doi.org/10.3390/jmse13112030 - 23 Oct 2025

Viewed by 848

Abstract

Very-low-frequency (VLF) passive sonar detection is one of the core technologies for maritime surveillance, although its performance is often severely affected by strong impulsive ocean ambient noise interference. This paper, for the first time, proposes a convolutional neural network (CNN) detection framework with hybrid Log-Mel spectrogram (Log-Mel) and Harmonic–Percussive Source Separation (HPSS) preprocessing. Aiming to highlight the detailed features of low frequencies in accordance with impulsive noise interference removal, the network was trained on a measured dataset in the South China Sea for a whole week by maximize the area under receiver operating characteristic curve (AUC) that corresponds to a false alarm probability of less than 0.1. The test results show that compared with a typical Short-Time Fourier Transform (STFT) input feature, the utilization of Log-Mel and HPSS can be superior, especially utilizing Log-Mel and HPSS(H) features at the same time. Validation with a set of measured moving ship data shows that the detection performance of the proposed hybrid Log-Mel and HPSS-aided CNN can be stable and significantly improve the remote passive sonar detection performance. Full article

(This article belongs to the Special Issue Advanced Studies in Marine Data Analysis)

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28 pages, 469 KB

Open AccessArticle

Scenario-Based Sensor Selection for Autonomous Maritime Systems: A Multi-Criteria Analysis of Sensor Configurations for Situational Awareness

by Florian Hoehner, Vincent Langenohl, Ould el Moctar and Thomas E. Schellin

J. Mar. Sci. Eng. 2025, 13(10), 2008; https://doi.org/10.3390/jmse13102008 - 19 Oct 2025

Cited by 1 | Viewed by 1263

Abstract

Effective operation of autonomous maritime systems requires sensor architectures tailored to mission-specific requirements, as key performance criteria like accuracy and energy consumption vary significantly by operational context. Against this background, this study develops a dual-stage, multi-criteria procedure to evaluate and assess individual sensors accounting for scenario-based requirements, using the TOPSIS algorithm as its core method. The first stage individually assesses sensors against scenario-specific requirements to generate context-aware weighting factors (

α_{i}^{(s)}

). In the second stage, these factors are used to evaluate the overall performance of seven predefined sensor suites across five distinct operational scenarios (e.g., ‘Coastal Surveillance’ or ‘Protection of Critical Infrastructure’). The procedure is complemented by an architectural robustness assessment that systematically captures the impact of component failures. This flexible approach serves as a generic decision framework for designing unmanned maritime systems across different mission profiles. By integrating key performance metrics and failure scenarios within a context of prioritized operational requirements, the dual-stage multi-criteria procedure enables more than just selecting an optimal configuration. It reveals the fundamental architectural design principles. Our results demonstrate that for precision-focused tasks such as ‘Coastal Surveillance’, specialized sensor suites combining electro-optical and laser rangefinder achieves the highest performance score (0.84). Conversely, for scenarios with balanced requirements like ‘Protection of Critical Infrastructure’, architectures based on functional complementarity (e.g., electro-optical and Radar, score (0.64)) prove most effective. A key finding is that maximizing sensor quantity does not guarantee optimal performance, as targeted, mission-specific configurations often outperform fully integrated systems. The significance of this study lies in providing a systematic framework that shifts the design paradigm from a ‘more is better’ approach to an intelligent, context-aware composition, enabling the development of truly robust and efficient sensor architectures for autonomous maritime systems. Full article

(This article belongs to the Special Issue Advanced Studies in Marine Data Analysis)

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25 pages, 7119 KB

Open AccessArticle

Long-Term Significant Wave Height Forecasting in the Western Atlantic Ocean Using Deep Learning

by Lu Zhang, Fan Jiang, Limin Huang, Dina Silva, Wenyang Duan and C. Guedes Soares

J. Mar. Sci. Eng. 2025, 13(10), 1968; https://doi.org/10.3390/jmse13101968 - 15 Oct 2025

Cited by 1 | Viewed by 1875

Abstract

This study presents a significant wave height correction model using deep learning techniques to enhance long-term wave forecast capabilities. The model utilises buoy measurements to assess the forecasting accuracy of the ECMWF 15-day forecast of significant wave height in the western Atlantic Ocean under various input conditions. The performance of different deep learning methods in modelling the wave forecast error is compared. The model predictions are validated against buoy data, revealing that the forecasting accuracy of the various deep learning methods is comparable. In addition, the model’s adaptability is examined for varying locations and water depths within the study area. The results demonstrate that the proposed method significantly improves the accuracy of the 15-day wave height forecasting and exhibits good adaptability to a vast sea area. Full article

(This article belongs to the Special Issue Advanced Studies in Marine Data Analysis)

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21 pages, 5927 KB

Open AccessArticle

Flow Control-Based Aerodynamic Enhancement of Vertical Axis Wind Turbines for Offshore Renewable Energy Deployment

by Huahao Ou, Qiang Zhang, Chun Li, Dinghong Lu, Weipao Miao, Huanhuan Li and Zifei Xu

J. Mar. Sci. Eng. 2025, 13(9), 1674; https://doi.org/10.3390/jmse13091674 - 31 Aug 2025

Cited by 1 | Viewed by 1435

Abstract

As wind energy development continues to expand toward nearshore and deep-sea regions, enhancing the aerodynamic efficiency of vertical axis wind turbines (VAWTs) in complex marine environments has become a critical challenge. To address this, a composite flow control strategy combining leading-edge suction and trailing-edge gurney flap is proposed. A two-dimensional unsteady numerical simulation framework is established based on CFD and the four-equation Transition SST (TSST) transition model. The key control parameters, including the suction slot position and width as well as the gurney flap height and width, are systematically optimized through orthogonal experimental design. The aerodynamic performance under single (suction or gurney flap) and composite control schemes is comprehensively evaluated. Results show that leading-edge suction effectively delays flow separation, while the gurney flap improves aerodynamic characteristics in the downwind region. Their synergistic effect significantly suppresses blade load fluctuations and enhances the wake structure, thereby improving wind energy capture. Compared to all other configurations, including suction-only and gurney flap-only blades, the composite control blade achieves the most significant increase in power coefficient across the entire tip speed ratio range, with an average improvement of 67.24%, demonstrating superior aerodynamic stability and strong potential for offshore applications. Full article

(This article belongs to the Special Issue Advanced Studies in Marine Data Analysis)

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26 pages, 4555 KB

Open AccessArticle

Influence of Geometric Effects on Dynamic Stall in Darrieus-Type Vertical-Axis Wind Turbines for Offshore Renewable Applications

by Qiang Zhang, Weipao Miao, Kaicheng Zhao, Chun Li, Linsen Chang, Minnan Yue and Zifei Xu

J. Mar. Sci. Eng. 2025, 13(7), 1327; https://doi.org/10.3390/jmse13071327 - 11 Jul 2025

Viewed by 1201

Abstract

The offshore implementation of vertical-axis wind turbines (VAWTs) presents a promising new paradigm for advancing marine wind energy utilization, owing to their omnidirectional wind acceptance, compact structural design, and potential for lower maintenance costs. However, VAWTs still face major aerodynamic challenges, particularly due to the pitching motion, where the angle of attack varies cyclically with the blade azimuth. This leads to strong unsteady effects and susceptibility to dynamic stalls, which significantly degrade aerodynamic performance. To address these unresolved issues, this study conducts a comprehensive investigation into the dynamic stall behavior and wake vortex evolution induced by Darrieus-type pitching motion (DPM). Quasi-three-dimensional CFD simulations are performed to explore how variations in blade geometry influence aerodynamic responses under unsteady DPM conditions. To efficiently analyze geometric sensitivity, a surrogate model based on a radial basis function neural network is constructed, enabling fast aerodynamic predictions. Sensitivity analysis identifies the curvature near the maximum thickness and the deflection angle of the trailing edge as the most influential geometric parameters affecting lift and stall behavior, while the blade thickness is shown to strongly impact the moment coefficient. These insights emphasize the pivotal role of blade shape optimization in enhancing aerodynamic performance under inherently unsteady VAWT operating conditions. Full article

(This article belongs to the Special Issue Advanced Studies in Marine Data Analysis)

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23 pages, 2708 KB

Open AccessArticle

Strategizing Artificial Intelligence Transformation in Smart Ports: Lessons from Busan’s Resilient AI Governance Model

by Jeong-min Lee, Min-seop Sim, Yul-seong Kim, Ha-ram Lim and Chang-hee Lee

J. Mar. Sci. Eng. 2025, 13(7), 1276; https://doi.org/10.3390/jmse13071276 - 30 Jun 2025

Cited by 4 | Viewed by 5228

Abstract

The global port and maritime industry is experiencing a new paradigm shift known as the artificial intelligence transformation (AX). Thus, domestic container-terminal companies should focus beyond mere automation to a paradigm shift in AI that encompasses operational strategy, organizational structure, system, and human resource management. This study proposes a resilience-based AX strategy and implementation system that allows domestic container-terminal companies to proactively respond to the upcoming changes in the global supply chain, thus securing sustainable competitiveness. In particular, we aim to design an AI-based governance model to establish a trust-based logistics supply chain (trust value chain). As a research method, the core risk factors of AX processes were scientifically identified via text-mining and fault-tree analysis, and a step-by-step execution strategy was established by applying a backcasting technique based on scenario planning. Additionally, by integrating social control theory with new governance theory, we designed a flexible, adaptable, and resilience-oriented AI governance system. The results of this study suggest that the AI paradigm shift should be promoted by enhancing the risk resilience, trust, and recovery of organizations. By suggesting AX strategies and policy as well as institutional improvement directions that embed resilience to secure the sustainable competitiveness of AI-based smart ports in Korea, this study serves as a basis for establishing strategies for the domestic container-terminal industry and for constructing a global leading model. Full article

(This article belongs to the Special Issue Advanced Studies in Marine Data Analysis)

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28 pages, 4445 KB

Open AccessArticle

Link Availability-Aware Routing Metric Design for Maritime Mobile Ad Hoc Network

by Shuaiheng Huai, Tianrui Liu, Yi Jiang, Yanpeng Dai, Feng Xue and Qing Hu

J. Mar. Sci. Eng. 2025, 13(6), 1184; https://doi.org/10.3390/jmse13061184 - 17 Jun 2025

Cited by 1 | Viewed by 1557

Abstract

A maritime mobile ad hoc network (M-MANET) is an essential part of the maritime communication network and plays a key role in many maritime scenarios. However, the topology of M-MANET dynamically changes with the movement of vessels, which leads to unstable link states and poses the risk of data transmission interruption. In this paper, a mobility model for small unmanned surface vessels based on smooth Gaussian semi-Markovian and a trajectory prediction method for large vessels based on a bi-directional long short-term memory network are proposed to better simulate the nodes’ movement in the M-MANET. Then, a link available based routing metric is proposed for M-MANET scenarios, which incorporates factors of mobility model and vessel trajectory. Experiments demonstrate that compared with the benchmark methods, the proposed mobility model depicts the movement characteristics of vessels more accurately, the proposed trajectory prediction method achieves higher prediction accuracy and stability, the proposed routing metric scheme has a reduction of 14.59% in end-to-end delay, a 1.54% increase in packet delivery fraction, and a 4.43% increase in network throughput on average. Full article

(This article belongs to the Special Issue Advanced Studies in Marine Data Analysis)

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27 pages, 24008 KB

Open AccessArticle

A Ship Heading Estimation Method Based on DeepLabV3+ and Contrastive Learning-Optimized Multi-Scale Similarity

by Weihao Tao, Yasong Luo, Jijin Tong, Qingtao Xia and Jianjing Qu

J. Mar. Sci. Eng. 2025, 13(6), 1085; https://doi.org/10.3390/jmse13061085 - 29 May 2025

Cited by 1 | Viewed by 1101

Abstract

With the rapid development of global maritime trade, high-precision ship heading estimation has become crucial for maritime traffic safety and intelligent shipping. To address the challenge of heading estimation from horizontal-view optical images, this study proposes a novel framework integrating DeepLabV3+ image segmentation with contrastive-learning-optimized multi-scale similarity matching. First, a cascaded image preprocessing method is developed, incorporating linear transformation, bilateral filtering, and the Multi-Scale Retinex with Color Restoration (MSRCR) algorithm to mitigate noise and haze interference and enhance image quality with improved target edge clarity. Subsequently, the DeepLabV3+ network is employed for the precise segmentation of ship targets, generating binarized contour maps for subsequent heading analysis. Based on actual ship dimensional parameters, 3D models are constructed and multi-angle rendered to establish a heading template library. The framework introduces the Multi-Scale Structural Similarity (MS-SSIM) algorithm enhanced by a triplet contrastive learning mechanism that dynamically optimizes feature weights across scales, thereby improving robustness against image degradation and partial occlusion. Experimental results demonstrate that under noise-free, noise-interfered, and mist-occluded conditions, the proposed method achieves mean heading estimation errors of 0.41°, 0.65°, and 0.88°, respectively, significantly outperforming the single-scale SSIM and fixed-weight MS-SSIM approaches. This verification confirms the method’s effectiveness and robustness, offering a novel technical solution for ship heading estimation in maritime surveillance and intelligent navigation systems. Full article

(This article belongs to the Special Issue Advanced Studies in Marine Data Analysis)

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