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Machine Learning Methodologies and Ocean Science
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Machine Learning Methodologies and Ocean Science

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 (10 April 2025) | Viewed by 10248

Special Issue Editors

Dr. Nawin Raj

E-Mail Website
Guest Editor
School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, Australia
Interests: artificial intelligence; machine learning; deep learning; oceanography; mathematical modelling; climate change; environmental modelling
Special Issues, Collections and Topics in MDPI journals
Dr. Lila Singh-Peterson

E-Mail Website
Guest Editor
School of Agriculture and Environmental Science, University of Southern Queensland, Springfield, QLD 4300, Australia
Interests: applied climate science; conceptual modelling of climate impacts; climate resilience
Special Issues, Collections and Topics in MDPI journals
Dr. Nathan Downs

E-Mail Website
Guest Editor
School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, Australia
Interests: climate change; artificial intelligence; machine learning; deep learning; atmospheric modelling; UV index; environmental modelling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Oceanic changes attributed to climate change are having significant impacts on marine and non-marine life forms across the globe. These direct and indirect changes manifest across time and space, requiring timely, accurate, and reliable data for decision making to identify and prioritise risks and to develop effective mitigation and adaptation strategies. For example, globally, sea level rise (SLR) is caused by the thermal expansion of ocean waters combined with freshwater input from melting glaciers and ice sheets. Between 1901 and 1990, the rate of global mean SLR was 1.35 mm/year greater than the rate of SLR in any century over the last 3000 years (IPCC, 2022). However, rates of SLR and other oceanic changes differ depending upon geographical factors and context. With the availability of ground-based and remote sensing datasets, machine learning techniques have provided highly accurate and reliable platforms to determine projections of future oceanic changes. Furthermore, the advancement of deep learning architecture has the capability to handle large datasets and extract features for highly accurate forecasting. Therefore, this Special Issue will provide a collection of research papers that present cutting-edge machine learning methodologies for the assessment and prediction of oceanic changes due to climate change.

Dr. Nawin Raj
Dr. Lila Singh Peterson
Dr. Nathan Downs
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

  • deep learning
  • climate change
  • sea level rise
  • remote sensing
  • artificial intelligence
  • machine learning
  • coastal changes
  • oceanography
  • wetland changes
  • mangrove changes

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Published Papers (9 papers)

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Research

23 pages, 6499 KiB  
Article
Enhancing Ocean Temperature and Salinity Reconstruction with Deep Learning: The Role of Surface Waves
by Xiaoyu Yu, Daling Li Yi and Peng Wang
J. Mar. Sci. Eng. 2025, 13(5), 910; https://doi.org/10.3390/jmse13050910 - 3 May 2025
Viewed by 342
Abstract
In oceanographic research, reconstructing the three-dimensional (3D) distribution of temperature and salinity is essential for understanding global climate dynamics, predicting marine environmental changes, and evaluating their impacts on ecosystems. While previous studies have largely concentrated on the effects of various modeling approaches on [...] Read more.
In oceanographic research, reconstructing the three-dimensional (3D) distribution of temperature and salinity is essential for understanding global climate dynamics, predicting marine environmental changes, and evaluating their impacts on ecosystems. While previous studies have largely concentrated on the effects of various modeling approaches on reconstructing oceanic variables, limited attention has been paid to the role of surface waves in reconstruction. This study, based on sea surface data, employs a deep learning-based neural network model, U-Net, to reconstruct 3D temperature and salinity across the North Pacific and Equatorial Pacific within the upper 200 m. The input of wave information includes the significant wave height (SWH), Langmuir number (La), and Langmuir enhancement factor (ε); the latter two indicate the strength of Langmuir turbulence, which promotes vertical mixing in the ocean surface layer and thereby affects profiles of temperature and salinity. The results indicate that incorporating wave information, particularly the La and ε, significantly enhances the model’s ability to reconstruct ocean temperature and salinity. This highlights the critical role of surface waves in enhancing the reconstruction of 3D ocean temperature and salinity. Full article
(This article belongs to the Special Issue Machine Learning Methodologies and Ocean Science)
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27 pages, 14080 KiB  
Article
Spatio-Temporal Prediction of Surface Remote Sensing Data in Equatorial Pacific Ocean Based on Multi-Element Fusion Network
by Tianliang Xu, Zhiquan Zhou, Chenxu Wang, Yingchun Li and Tian Rong
J. Mar. Sci. Eng. 2025, 13(4), 755; https://doi.org/10.3390/jmse13040755 - 10 Apr 2025
Viewed by 341
Abstract
A basic feature of El Niño is an abnormal increase in the surface temperature of the equatorial Pacific Ocean, which can throw ocean–atmosphere interactions out of balance, resulting in heavy rainfall and severe storms. This climate anomaly causes different levels of impacts worldwide, [...] Read more.
A basic feature of El Niño is an abnormal increase in the surface temperature of the equatorial Pacific Ocean, which can throw ocean–atmosphere interactions out of balance, resulting in heavy rainfall and severe storms. This climate anomaly causes different levels of impacts worldwide, such as causing droughts in some regions and excessive rainfall in others. Therefore, it is important to determine the formation of El Niño by predicting the changes in the sea surface temperature (SST) in the equatorial Pacific Ocean. In this paper, we propose a multi-element fusion network model based on convolutional long short-term memory (ConvLSTM) and an attention mechanism to predict the SST and analyze the effects of different elemental inputs on the model’s prediction performance using the prediction results. The experimental results show that using the sea surface wind (SSW) and sea level anomaly (SLA) as multi-element inputs to predict the SST overcame the shortcomings of the single-element forecast model, and the prediction accuracy of the two-element fusion model was better than that of the three-element fusion model. In the two-element fusion model, using the SSW as an input predicted the SST with a lower prediction error than using the SLA as an input and had better prediction performance compared with other benchmark models. For predicting the SST in the equatorial Pacific Ocean, the monthly average root mean square error (RMSE) was mainly concentrated in the range of 0.4–0.8 °C, and the regions with a larger error dispersion were located in the spatial range of 5° S–5° N and 130° W–90° W, and the monthly average regional RMSE was mainly concentrated in the range of 0.5–1 °C. Finally, we also validated the prediction performance of the model for the SST in El Niño and La Niña years, and the prediction results of the model in La Niña years were better than those in El Niño years. Full article
(This article belongs to the Special Issue Machine Learning Methodologies and Ocean Science)
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15 pages, 4110 KiB  
Article
Hyperspectral Image-Based Identification of Maritime Objects Using Convolutional Neural Networks and Classifier Models
by Dongmin Seo, Daekyeom Lee, Sekil Park and Sangwoo Oh
J. Mar. Sci. Eng. 2025, 13(1), 6; https://doi.org/10.3390/jmse13010006 - 24 Dec 2024
Cited by 1 | Viewed by 1216
Abstract
The identification of maritime objects is crucial for ensuring navigational safety, enabling effective environmental monitoring, and facilitating efficient maritime search and rescue operations. Given its ability to provide detailed spectral information, hyperspectral imaging has emerged as a powerful tool for analyzing the physical [...] Read more.
The identification of maritime objects is crucial for ensuring navigational safety, enabling effective environmental monitoring, and facilitating efficient maritime search and rescue operations. Given its ability to provide detailed spectral information, hyperspectral imaging has emerged as a powerful tool for analyzing the physical and chemical properties of target objects. This study proposes a novel maritime object identification framework that integrates hyperspectral imaging with machine learning models. Hyperspectral data from six ports in South Korea were collected using airborne sensors and subsequently processed into spectral statistics and RGB images. The processed data were then analyzed using classifier and convolutional neural network (CNN) models. The results obtained in this study show that CNN models achieved an average test accuracy of 90%, outperforming classifier models, which achieved 83%. Among the CNN models, EfficientNet B0 and Inception V3 demonstrated the best performance, with Inception V3 achieving a category-specific accuracy of 97% when weights were excluded. This study presents a robust and efficient framework for marine surveillance utilizing hyperspectral imaging and machine learning, offering significant potential for advancing marine detection and monitoring technologies. Full article
(This article belongs to the Special Issue Machine Learning Methodologies and Ocean Science)
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26 pages, 15156 KiB  
Article
Research on the Lossless Data Compression System of the Argo Buoy Based on BiLSTM-MHSA-MLP
by Sumin Guo, Wenqi Zhang, Yuhong Zheng, Hongyu Li, Yilin Yang and Jiayi Xu
J. Mar. Sci. Eng. 2024, 12(12), 2298; https://doi.org/10.3390/jmse12122298 - 13 Dec 2024
Viewed by 842
Abstract
This study addresses the issues of the limited data storage capacity of Argo buoys and satellite communication charges on the basis of data volume by proposing a block lossless data compression method that combines bidirectional long short-term memory networks and multi-head self-attention with [...] Read more.
This study addresses the issues of the limited data storage capacity of Argo buoys and satellite communication charges on the basis of data volume by proposing a block lossless data compression method that combines bidirectional long short-term memory networks and multi-head self-attention with a multilayer perceptron (BiLSTM-MHSA-MLP). We constructed an Argo buoy data compression system using the main buoy control board, Jetson nano development board, and the BeiDou-3 satellite transparent transmission module. By processing input sequences bidirectionally, BiLSTM enhances the understanding of the temporal relationships within profile data, whereas the MHSA processes the outputs of the BiLSTM layer in parallel to obtain richer representations. Building on this preliminary probability prediction model, a multilayer perceptron (MLP) and a block length parameter (block_len) are introduced to achieve block compression during training, dynamically updating the model and optimizing symbol probability distributions for more accurate predictions. Experiments conducted on multiple 4000 m single-batch profile datasets from both the PC and Jetson nano platforms demonstrate that this method achieves a lower compression ratio, shorter compression time, and greater specificity. This approach significantly reduces the communication time between Argo buoys and satellites, laying a foundation for the future integration of Jetson Nano into Argo buoys for real-time data compression. Full article
(This article belongs to the Special Issue Machine Learning Methodologies and Ocean Science)
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17 pages, 5452 KiB  
Article
Application of Hyperspectral Image for Monitoring in Coastal Area with Deep Learning: A Case Study of Green Algae on Artificial Structure
by Tae-Ho Kim, Jee Eun Min, Hye Min Lee, Kuk Jin Kim and Chan-Su Yang
J. Mar. Sci. Eng. 2024, 12(11), 2042; https://doi.org/10.3390/jmse12112042 - 11 Nov 2024
Cited by 1 | Viewed by 1116
Abstract
Remote sensing is a powerful technique for classifying and quantifying objects. However, the elaborate classification of objects in coastal waters with complex structures is still challenging due to the high possibility of class mixing. The classification through the hyperspectral images can be a [...] Read more.
Remote sensing is a powerful technique for classifying and quantifying objects. However, the elaborate classification of objects in coastal waters with complex structures is still challenging due to the high possibility of class mixing. The classification through the hyperspectral images can be a reasonable alternative to problems related to such precise classification work because it has high spectral resolution over a wide bandwidth. This study introduced the results of the case study using a novel method to classify green algae on an artificial structure based on hyperspectral data and deep-learning models. The spectral characteristics of the attached green algae on the artificial structure were observed using a ground-based hyperspectral camera. The observed image had a total of three classes (concrete, dense green algae, and sparse green algae). A certain area of the image was used as learning data to create classification models for three classes. The classification models were created from one machine-learning (support vector machine, SVM) and two deep-learning models (convolutional neural network, CNN; and dense convolutional network, DenseNet). As a result, the performance for the classification results of green algae predicted from two deep-learning models was higher than that of the machine-learning model. Additionally, the deep-learning model successfully classified the interface area between concrete and green algae. This study suggests that the combination of hyperspectral data and deep learning could enable more precise classification of objects in coastal areas. Full article
(This article belongs to the Special Issue Machine Learning Methodologies and Ocean Science)
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23 pages, 7024 KiB  
Article
A Multi-Spatial Scale Ocean Sound Speed Prediction Method Based on Deep Learning
by Yu Liu, Benjun Ma, Zhiliang Qin, Cheng Wang, Chao Guo, Siyu Yang, Jixiang Zhao, Yimeng Cai and Mingzhe Li
J. Mar. Sci. Eng. 2024, 12(11), 1943; https://doi.org/10.3390/jmse12111943 - 31 Oct 2024
Viewed by 1057
Abstract
As sound speed is a fundamental parameter of ocean acoustic characteristics, its prediction is a central focus of underwater acoustics research. Traditional numerical and statistical forecasting methods often exhibit suboptimal performance under complex conditions, whereas deep learning approaches demonstrate promising results. However, these [...] Read more.
As sound speed is a fundamental parameter of ocean acoustic characteristics, its prediction is a central focus of underwater acoustics research. Traditional numerical and statistical forecasting methods often exhibit suboptimal performance under complex conditions, whereas deep learning approaches demonstrate promising results. However, these methodologies fall short in adequately addressing multi-spatial coupling effects and spatiotemporal weighting, particularly in scenarios characterized by limited data availability. To investigate the interactions across multiple spatial scales and to achieve accurate predictions, we propose the STA-ConvLSTM framework that integrates spatiotemporal attention mechanisms with convolutional long short-term memory neural networks (ConvLSTM). The core concept involves accounting for the coupling effects among various spatial scales while extracting temporal and spatial information from the data and assigning appropriate weights to different spatiotemporal entities. Furthermore, we introduce an interpolation method for ocean temperature and salinity data based on the KNN algorithm to enhance dataset resolution. Experimental results indicate that STA-ConvLSTM provides precise predictions of sound speed. Specifically, relative to the measured data, it achieved a root mean square error (RMSE) of approximately 0.57 m/s and a mean absolute error (MAE) of about 0.29 m/s. Additionally, when compared to single-dimensional spatial analysis, incorporating multi-spatial scale considerations yielded superior predictive performance. Full article
(This article belongs to the Special Issue Machine Learning Methodologies and Ocean Science)
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18 pages, 3291 KiB  
Article
Gradient Boosted Trees and Denoising Autoencoder to Correct Numerical Wave Forecasts
by Ivan Yanchin and C. Guedes Soares
J. Mar. Sci. Eng. 2024, 12(9), 1573; https://doi.org/10.3390/jmse12091573 - 6 Sep 2024
Cited by 1 | Viewed by 1025
Abstract
This paper is dedicated to correcting the WAM/ICON numerical wave model predictions by reducing the residue between the model’s predictions and the actual buoy observations. The two parameters used in this paper are significant wave height and wind speed. The paper proposes two [...] Read more.
This paper is dedicated to correcting the WAM/ICON numerical wave model predictions by reducing the residue between the model’s predictions and the actual buoy observations. The two parameters used in this paper are significant wave height and wind speed. The paper proposes two machine learning models to solve this task. Both models are multioutput models and correct the significant wave height and wind speed simultaneously. The first machine learning model is based on gradient boosted trees, which is trained to predict the residue between the model’s forecasts and the actual buoy observations using the other parameters predicted by the numerical model as inputs. This paper demonstrates that this model can significantly reduce errors for all used geographical locations. This paper also uses SHapley Additive exPlanation values to investigate the influence that the numerically predicted wave parameters have when the machine learning model predicts the residue. To design the second model, it is assumed that the residue can be modelled as noise added to the actual values. Therefore, this paper proposes to use the denoising autoencoder to remove this noise from the numerical model’s prediction. The results demonstrate that denoising autoencoders can remove the noise for the wind speed parameter, but their performance is poor for the significant wave height. This paper provides some explanations as to why this may happen. Full article
(This article belongs to the Special Issue Machine Learning Methodologies and Ocean Science)
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18 pages, 1516 KiB  
Article
Post-Processing Maritime Wind Forecasts from the European Centre for Medium-Range Weather Forecasts around the Korean Peninsula Using Support Vector Regression and Principal Component Analysis
by Seung-Hyun Moon, Do-Youn Kim and Yong-Hyuk Kim
J. Mar. Sci. Eng. 2024, 12(8), 1360; https://doi.org/10.3390/jmse12081360 - 9 Aug 2024
Viewed by 935
Abstract
Accurate wind data are crucial for successful search and rescue (SAR) operations on the sea surface in maritime accidents, as survivors or debris tend to drift with the wind. As maritime accidents frequently occur outside the range of wind stations, SAR operations heavily [...] Read more.
Accurate wind data are crucial for successful search and rescue (SAR) operations on the sea surface in maritime accidents, as survivors or debris tend to drift with the wind. As maritime accidents frequently occur outside the range of wind stations, SAR operations heavily rely on wind forecasts generated by numerical models. However, numerical models encounter delays in generating results due to spin-up issues, and their predictions can sometimes exhibit inherent biases caused by geographical factors. To overcome these limitations, we reviewed the observations for the first 24 h of the 72-hour forecast from the ECMWF and then post-processed the forecast for the remaining 48 h. By effectively reducing the dimensionality of input variables comprising observation and forecast data using principal component analysis, we improved wind predictions with support vector regression. Our model achieved an average RMSE improvement of 16.01% compared to the original forecast from the ECMWF. Furthermore, it achieved an average RMSE improvement of 5.42% for locations without observation data by employing a model trained on data from the nearest wind station and then applying an adaptive weighting scheme to the output of that model. Full article
(This article belongs to the Special Issue Machine Learning Methodologies and Ocean Science)
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16 pages, 4411 KiB  
Article
Real-Time Infrared Sea–Sky Line Region Detection in Complex Environment Based on Deep Learning
by Yongfei Wang, Fan Li, Jianhui Zhao and Jian Fu
J. Mar. Sci. Eng. 2024, 12(7), 1092; https://doi.org/10.3390/jmse12071092 - 28 Jun 2024
Viewed by 1137
Abstract
Fast and accurate infrared (IR) sea–sky line region (SSLR) detection can improve the early warning capability of the small targets that appear in the remote sea–sky junction. However, the traditional algorithms struggle to achieve high precision, while the learning-based ones have low detection [...] Read more.
Fast and accurate infrared (IR) sea–sky line region (SSLR) detection can improve the early warning capability of the small targets that appear in the remote sea–sky junction. However, the traditional algorithms struggle to achieve high precision, while the learning-based ones have low detection speed. To overcome these problems, a novel learning-based algorithm is proposed; rather than detecting the sea–sky line first, the proposed algorithm directly provides SSLR, which mainly consists of three parts: Firstly, an IR sea–sky line region detection module (ISRDM) is proposed, which combines strip pooling and the connection mode of a cross-stage partial network to extract the features of the SSLR target, with an unbalanced aspect ratio, more specifically, thus improving the detection accuracy. Secondly, a lightweight backbone is presented to reduce the parameters of the model and, therefore, improve the inference speed. Finally, a Detection Head Based on the spatial-aware attention module (SAMHead) is designed to enhance the perception ability of the SSLR and further reduce the inference time. Extensive experiments conducted on three datasets with more than 26,000 frames show that the proposed algorithm achieved approximately 80% average precision (AP), outperforms the state-of-the-art algorithms in accuracy, and can realize real-time detection. Full article
(This article belongs to the Special Issue Machine Learning Methodologies and Ocean Science)
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