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Innovations in Maritime Safety: Integrating Artificial Intelligence, Human Factors, and System Resilience

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Marine Science and Engineering".

Deadline for manuscript submissions: 20 June 2026 | Viewed by 724

Special Issue Editor

Special Issue Information

Dear Colleagues,

The maritime industry is undergoing a profound transformation driven by digitalization, automation, and artificial intelligence (AI). These innovations promise significant advances in efficiency and sustainability, but they also introduce new challenges for safety management and human reliability at sea. Integrating AI into maritime operations requires a deep understanding of system resilience, human–machine interactions, and the dynamic behaviors of complex maritime systems under stress.

Human factors remain a cornerstone of maritime safety. Even in increasingly automated environments, human operators play essential roles in supervision, decision-making, and emergency response. Balancing technological innovation with human performance and resilience is therefore a key challenge for the future of safe and sustainable shipping.

This Special Issue aims to bring together cutting-edge research and applied developments that address the integration of AI, human factors, and system resilience in maritime safety. We invite contributions that explore innovative concepts, models, and methodologies that will enhance the prevention, detection, and mitigation of risks in maritime transport, offshore operations, and port systems.

The aims of this Special Issue are as follows:

  • Highlight recent advances in artificial intelligence applications for maritime safety and risk assessment.
  • Promote interdisciplinary approaches that combine human factors engineering, system reliability, and digital technologies.
  • Provide a platform for experimental, modeling, and case-based research on maritime system resilience.
  • Foster discussion on regulatory, ethical, and operational frameworks for the safe adoption of AI in maritime contexts.

Suggested Themes and Article Types for Submission

In this Special Issue, original research articles and comprehensive reviews are welcome. Research areas may include, but are not limited to, the following:

  1. AI-based decision support systems for navigation, maintenance, and emergency response.
  2. Human–machine interactions, ergonomics, and adaptive automation in ship operations.
  3. Predictive analytics and digital twins for safety and reliability assessment.
  4. Maritime cybersecurity and AI-enabled resilience strategies.
  5. Simulation and training systems that incorporate AI and virtual reality.
  6. Risk modeling and probabilistic safety assessment using AI techniques.
  7. Human performance and situational awareness in autonomous ship control.
  8. Regulatory frameworks and ethical considerations in AI-driven maritime operations.
  9. Integrating resilience engineering principles into the design and operation of ships.
  10. Case studies and best practices on AI adoption in maritime safety management.
  11. Safety, risk, and fire hazards associated with onboard energy storage systems, including lithium-ion batteries.

We look forward to receiving your contributions to this Special Issue, which aims to advance both the theoretical understanding and practical implementation of innovations in maritime safety through the integration of artificial intelligence, human factors, and system resilience.

Prof. Dr. José A. Orosa García
Guest Editor

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. Applied Sciences 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 2400 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

  • artificial intelligence
  • marine safety
  • merchant ships
  • human factor
  • resilience
  • new technologies

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Published Papers (1 paper)

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Research

28 pages, 12924 KB  
Article
Research on a Wave Elevation Reconstruction Method at Fixed Positions
by Zhiqiang Jiang, Yongyan Ma, Yong Wu and Weijia Li
Appl. Sci. 2026, 16(2), 898; https://doi.org/10.3390/app16020898 - 15 Jan 2026
Viewed by 390
Abstract
Accurate wave detection is essential for reliable ship motion prediction and the safety of offshore operations. Wave buoys are widely deployed as key instruments for capturing wave characteristics. However, buoys drift due to the waves and currents, resulting in errors in reconstructed wave [...] Read more.
Accurate wave detection is essential for reliable ship motion prediction and the safety of offshore operations. Wave buoys are widely deployed as key instruments for capturing wave characteristics. However, buoys drift due to the waves and currents, resulting in errors in reconstructed wave elevation. To address this challenge, a fixed-position wave-elevation reconstruction method is proposed in this paper. First, a temporal convolutional network (TCN) module is integrated with a gated recurrent unit (GRU) network to efficiently capture the nonlinear relationship between buoy motion and wave elevation, enabling simultaneous wave elevation reconstruction and dynamic deviation compensation. Second, a static deviation compensation algorithm developed from wave theory is introduced to convert the spatial deviation into temporal misalignment. The proposed method is evaluated in both time and frequency domains across various sea conditions. Results demonstrate that the proposed method effectively compensates for deviations and achieves accurate reconstruction of wave elevation at the target position. In higher sea states, accurate reconstruction is maintained even at large static deviations, with relative errors typically within 10–15%. Frequency-domain analysis shows that coherence approaches 1 near the spectral peak and below 0.3 at higher frequencies, indicating that the dominant wave components are accurately reconstructed and that high-frequency noise has a limited impact on overall accuracy. Full article
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