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The Application of Artificial Intelligence and Machine Learning in a...
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The Application of Artificial Intelligence and Machine Learning in a Marine Context - Edition II

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312).

Deadline for manuscript submissions: 25 December 2025 | Viewed by 3855

Special Issue Editors

Prof. Dr. Fausto Pedro García Márquez

E-Mail Website
Guest Editor
Ingneium Research Group, Universidad de Castilla-La Mancha, Ciudad Real, Spain
Interests: artificial intelligence; maintenance; management; advanced analytics; data science
Special Issues, Collections and Topics in MDPI journals
Dr. Marco Cococcioni

E-Mail Website
Guest Editor
Department of Information Engineering, University of Pisa, Via Girolamo Caruso, 16, 56122 Pisa, PI, Italy
Interests: artificial intelligence; genetic fuzzy systems; multiobjective optimization; decision support systems; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue covers research on the applications of artificial intelligence and machine learning methods using data from marine contexts. Studies exploring applications of these methods to different professional areas, such as fisheries, engineering, economy, and society, will be accepted. This Special Issue welcomes multi-disciplinary works combining marine, engineering and computer science approaches. This work relates to the broader contexts of digital twins of the ocean, big data, data mining, and open science for marine data; contributions are expected to relate to these areas.

Prof. Dr. Fausto Pedro García Márquez
Dr. Marco Cococcioni
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

  • artificial intelligence
  • machine learning
  • marine science
  • ecological modelling
  • ecological niche modelling
  • digital twins of the ocean
  • fisheries models
  • data mining
  • big data
  • open science

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

Related Special Issue

Published Papers (4 papers)

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Research

20 pages, 3921 KB  
Article
Design of an Experimental Teaching Platform for Flow-Around Structures and AI-Driven Modeling in Marine Engineering
by Hongyang Zhao, Bowen Zhao, Xu Liang and Qianbin Lin
J. Mar. Sci. Eng. 2025, 13(9), 1761; https://doi.org/10.3390/jmse13091761 - 11 Sep 2025
Viewed by 109
Abstract
Flow past bluff bodies (e.g., circular cylinders) forms a canonical context for teaching external flow separation, vortex shedding, and the coupling between surface pressure and hydrodynamic forces in offshore engineering. Conventional laboratory implementations, however, often fragment local and global measurements, delay data feedback, [...] Read more.
Flow past bluff bodies (e.g., circular cylinders) forms a canonical context for teaching external flow separation, vortex shedding, and the coupling between surface pressure and hydrodynamic forces in offshore engineering. Conventional laboratory implementations, however, often fragment local and global measurements, delay data feedback, and omit intelligent modeling components, thereby limiting the development of higher-order cognitive skills and data literacy. We present a low-cost, modular, data-enabled instructional hydrodynamics platform that integrates a transparent recirculating water channel, multi-point synchronous circumferential pressure measurements, global force acquisition, and an artificial neural network (ANN) surrogate. Using feature vectors composed of Reynolds number, angle of attack, and submergence depth, we train a lightweight AI model for rapid prediction of drag and lift coefficients, closing a loop of measurement, prediction, deviation diagnosis, and feature refinement. In the subcritical Reynolds regime, the measured circumferential pressure distribution for a circular cylinder and the drag and lift coefficients for a rectangular cylinder agree with empirical correlations and published benchmarks. The ANN surrogate attains a mean absolute percentage error of approximately 4% for both drag and lift coefficients, indicating stable, physically interpretable performance under limited feature inputs. This platform will facilitate students’ cross-domain transfer spanning flow physics mechanisms, signal processing, feature engineering, and model evaluation, thereby enhancing inquiry-driven and critical analytical competencies. Key contributions include the following: (i) a synchronized local pressure and global force dataset architecture; (ii) embedding a physics-interpretable lightweight ANN surrogate in a foundational hydrodynamics experiment; and (iii) an error-tracking, iteration-oriented instructional workflow. The platform provides a replicable pathway for transitioning offshore hydrodynamics laboratories toward an integrated intelligence-plus-data literacy paradigm and establishes a foundation for future extensions to higher Reynolds numbers, multiple body geometries, and physics-constrained neural networks. Full article
(This article belongs to the Special Issue The Application of Artificial Intelligence and Machine Learning in a Marine Context - Edition II)
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25 pages, 8011 KB  
Article
Inversion of Seawater Sound Speed Profile Based on Hamiltonian Monte Carlo Algorithm
by Jiajia Zhao, Shuqing Ma and Qiang Lan
J. Mar. Sci. Eng. 2025, 13(9), 1670; https://doi.org/10.3390/jmse13091670 - 30 Aug 2025
Viewed by 353
Abstract
Inverting seawater sound speed profiles (SSPs) using Bayesian methods enables optimal parameter estimation and provides a quantitative assessment of uncertainty by analyzing the posterior distribution of target parameters. However, in nonlinear geophysical inversion problems like acoustic tomography, calculating the posterior distribution remains challenging. [...] Read more.
Inverting seawater sound speed profiles (SSPs) using Bayesian methods enables optimal parameter estimation and provides a quantitative assessment of uncertainty by analyzing the posterior distribution of target parameters. However, in nonlinear geophysical inversion problems like acoustic tomography, calculating the posterior distribution remains challenging. In this study, a Bayesian framework is used to construct the posterior distribution of target parameters based on acoustic travel-time data and prior information. A Hamiltonian Monte Carlo (HMC) approach is developed for SSP inversion, offering an effective solution to the computational issues associated with complex posterior distributions. The HMC algorithm has a strong physical basis in exploring distributions, allowing for accurate characterization of physical correlations among target parameters. It also achieves sufficient sampling of heavy-tailed probabilities, enabling a thorough analysis of the target distribution characteristics and overcoming the low efficiency often seen in traditional methods. The SSP dataset was created using temperature–salinity profile data from the Hybrid Coordinate Ocean Model (HYCOM) and empirical formulas for SSP. Experiments with acoustic propagation time data from the Kuroshio Extension System Study (KESS) confirmed the feasibility of the HMC method in SSP inversion. Full article
(This article belongs to the Special Issue The Application of Artificial Intelligence and Machine Learning in a Marine Context - Edition II)
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19 pages, 4962 KB  
Article
A Prediction of the Shooting Trajectory for a Tuna Purse Seine Using the Double Deep Q-Network (DDQN) Algorithm
by Daeyeon Cho and Jihoon Lee
J. Mar. Sci. Eng. 2025, 13(3), 530; https://doi.org/10.3390/jmse13030530 - 10 Mar 2025
Viewed by 909
Abstract
The purse seine is a fishing method in which a net is used to encircle a fish school, capturing isolated fish by tightening a purse line at the bottom of the net. Tuna purse seine operations are technically complex, requiring the evaluation of [...] Read more.
The purse seine is a fishing method in which a net is used to encircle a fish school, capturing isolated fish by tightening a purse line at the bottom of the net. Tuna purse seine operations are technically complex, requiring the evaluation of fish movements, vessel dynamics, and their interactions, with success largely dependent on the expertise of the crew. In particular, efficiency in terms of highly complex tasks, such as calculating the shooting trajectory during fishing operations, varies significantly based on the fisher’s skill level. To address this challenge, developing techniques to support less experienced fishers is necessary, particularly for operations targeting free-swimming fish schools, which are more difficult to capture compared to those utilizing Fish Aggregating Devices (FADs). This study proposes a method for predicting shooting trajectories using the Double Deep Q-Network (DDQN) algorithm. Observation states, actions, and reward functions were designed to identify optimal scenarios for shooting, and the catchability of the predicted trajectories was evaluated through gear behavior analysis. The findings of this study are expected to aid in the development of a trajectory prediction system for inexperienced fishers and serve as foundational data for automating purse seine fishing systems. Full article
(This article belongs to the Special Issue The Application of Artificial Intelligence and Machine Learning in a Marine Context - Edition II)
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22 pages, 61756 KB  
Article
Evaluation of Deep Learning Models for Polymetallic Nodule Detection and Segmentation in Seafloor Imagery
by Gabriel Loureiro, André Dias, José Almeida, Alfredo Martins and Eduardo Silva
J. Mar. Sci. Eng. 2025, 13(2), 344; https://doi.org/10.3390/jmse13020344 - 13 Feb 2025
Viewed by 1209
Abstract
Climate change has led to the need to transition to clean technologies, which depend on an number of critical metals. These metals, such as nickel, lithium, and manganese, are essential for developing batteries. However, the scarcity of these elements and the risks of [...] Read more.
Climate change has led to the need to transition to clean technologies, which depend on an number of critical metals. These metals, such as nickel, lithium, and manganese, are essential for developing batteries. However, the scarcity of these elements and the risks of disruptions to their supply chain have increased interest in exploiting resources on the deep seabed, particularly polymetallic nodules. As the identification of these nodules must be efficient to minimize disturbance to the marine ecosystem, deep learning techniques have emerged as a potential solution. Traditional deep learning methods are based on the use of convolutional layers to extract features, while recent architectures, such as transformer-based architectures, use self-attention mechanisms to obtain global context. This paper evaluates the performance of representative models from both categories across three tasks: detection, object segmentation, and semantic segmentation. The initial results suggest that transformer-based methods perform better in most evaluation metrics, but at the cost of higher computational resources. Furthermore, recent versions of You Only Look Once (YOLO) have obtained competitive results in terms of mean average precision. Full article
(This article belongs to the Special Issue The Application of Artificial Intelligence and Machine Learning in a Marine Context - Edition II)
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