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Earth System Modeling, Data Assimilation, Artificial Intelligence, Deep Learning and Ocean Information Engineering II

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Physical Oceanography".

Deadline for manuscript submissions: closed (15 April 2024) | Viewed by 9313

Special Issue Editors

Prof. Dr. Shaoqing Zhang

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Guest Editor
Key Laboratory of Physical Oceanography, Ministry of Education, Ocean University of China, Qingdao, China
Interests: coupled modeling; coupled model data assimilation; weather-climate predictability; parameter estimation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yuxin Zhao

E-Mail Website
Guest Editor
College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China
Interests: intelligent systems; information fusion; ocean information engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Hao Zuo

E-Mail Website
Guest Editor
European Centre for Medium-Range Weather Forecasts, Reading, UK
Interests: ocean data assimilation; ocean analysis; climate reanalysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Junyu Dong

E-Mail Website
Guest Editor
The College of Information Science and Engineering, Ocean University of China, Qingdao, China
Interests: ocean data science; machine learning; computer vision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Numerous questions are emerging in this relative bloom in the data industry: Should the development of artificial intelligence and deep learning (AIDL) be driven by data, scientific models or information estimation theory? How can AIDL benefit more from, as well as advance, science and technology? A science-driven AIDL is evidently a promising track. As a matter of fact, AIDL originated from our understanding of the natural world—mathematical modeling with dynamics and physics, data assimilation—with Bayes’ Theorem guiding combinations of models and data, as well as advanced deep neural network algorithms.

In this Special Issue, we call for papers that cover and address recent advances in modeling, data assimilation, and parameter estimation, as well as AIDL-associated research and development in geoscience research and applications, including advanced modeling, data assimilation, and deep neural network algorithms and data mining in the Earth system. Additionally, we intend to cover advanced topics such as data-based parameter optimization, AIDL-induced parameterization, etc. We address the concept that science-driven AIDL development can help to improve our understanding of dynamics and physics, thus furthering the advances of science and technology. Potential topics include, but are not limited to:

  • Modeling, data assimilation, and parameter estimation;
  • Bayes’ theorem-based AIDL algorithms;
  • Data-assimilation-induced AIDL algorithms;
  • Model parameter estimation and AIDL;
  • Advanced deep neural network algorithms;
  • Climate downscaling and evaluation with neural networks;
  • AIDL-induced climate and chemistry modeling and parameterization;
  • Advanced AIDL algorithms induced from modeling mesoscale to sub-mesoscale physical processes;
  • AIDL-driven cloud and microphysics expressions;
  • Data-based parameter optimization applied to AIDL algorithms;
  • Data mining in the Earth system (e.g., optimal translation of native Earth system observations into user-specific information).

Prof. Dr. Shaoqing Zhang
Prof. Dr. Yuxin Zhao
Dr. Hao Zuo
Prof. Dr. Junyu Dong
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

  • earth system modeling
  • data assimilation
  • artificial intelligence
  • deep learning
  • ocean information engineering

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Related Special Issue

Published Papers (6 papers)

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Research

20 pages, 7753 KiB  
Article
SICFormer: A 3D-Swin Transformer for Sea Ice Concentration Prediction
by Zhuoqing Jiang, Bing Guo, Huihui Zhao, Yangming Jiang and Yi Sun
J. Mar. Sci. Eng. 2024, 12(8), 1424; https://doi.org/10.3390/jmse12081424 - 17 Aug 2024
Viewed by 892
Abstract
Sea ice concentration (SIC) is an important dimension for characterising the geographical features of the pan-Arctic region. Trends in SIC bring new opportunities for human activities in the Arctic region. In this paper, we propose a deep learning technology-based sea ice concentration prediction [...] Read more.
Sea ice concentration (SIC) is an important dimension for characterising the geographical features of the pan-Arctic region. Trends in SIC bring new opportunities for human activities in the Arctic region. In this paper, we propose a deep learning technology-based sea ice concentration prediction model, SICFormer, which can realise end-to-end daily sea ice concentration prediction. Specifically, the model uses a 3D-Swin Transformer as an encoder and designs a decoder to reconstruct the predicted image based on PixelShuffle. This is a new model architecture that we have proposed. Single-day SIC data from the National Snow and Ice Data Center (NSIDC) for the years 2006 to 2022 are utilised. The results of 8-day short-term prediction experiments show that the average Mean Absolute Error (MAE) of the SICFormer model on the test set over the 5 years is 1.89%, the Root Mean Squared Error (RMSE) is 5.99%, the Mean Absolute Percentage Error (MAPE) is 4.32%, and the Nash–Sutcliffe Efficiency (NSE) is 0.98. Furthermore, the current popular deep learning models for spatio-temporal prediction are employed as a point of comparison given their proven efficacy on numerous public datasets. The comparison experiments show that the SICFormer model achieves the best overall performance. Full article
(This article belongs to the Special Issue Earth System Modeling, Data Assimilation, Artificial Intelligence, Deep Learning and Ocean Information Engineering II)
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12 pages, 2690 KiB  
Article
Underwater Image Enhancement Based on Light Field-Guided Rendering Network
by Chia-Hung Yeh, Yu-Wei Lai, Yu-Yang Lin, Mei-Juan Chen and Chua-Chin Wang
J. Mar. Sci. Eng. 2024, 12(7), 1217; https://doi.org/10.3390/jmse12071217 - 19 Jul 2024
Cited by 2 | Viewed by 956
Abstract
Underwater images often encounter challenges such as attenuation, color distortion, and noise caused by artificial lighting sources. These imperfections not only degrade image quality but also impose constraints on related application tasks. Improving underwater image quality is crucial for underwater activities. However, obtaining [...] Read more.
Underwater images often encounter challenges such as attenuation, color distortion, and noise caused by artificial lighting sources. These imperfections not only degrade image quality but also impose constraints on related application tasks. Improving underwater image quality is crucial for underwater activities. However, obtaining clear underwater images has been a challenge, because scattering and blur hinder the rendering of true underwater colors, affecting the accuracy of underwater exploration. Therefore, this paper proposes a new deep network model for single underwater image enhancement. More specifically, our framework includes a light field module (LFM) and sketch module, aiming at the generation of a light field map of the target image for improving the color representation and preserving the details of the original image by providing contour information. The restored underwater image is gradually enhanced, guided by the light field map. The experimental results show the better image restoration effectiveness, both quantitatively and qualitatively, of the proposed method with a lower (or comparable) computing cost, compared with the state-of-the-art approaches. Full article
(This article belongs to the Special Issue Earth System Modeling, Data Assimilation, Artificial Intelligence, Deep Learning and Ocean Information Engineering II)
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16 pages, 2446 KiB  
Article
Nonparametric Modelling of Ship Dynamics Using Puma Optimizer Algorithm-Optimized Twin Support Vector Regression
by Lichao Jiang, Zhi Zhang, Lingyun Lu, Xiaobing Shang and Wei Wang
J. Mar. Sci. Eng. 2024, 12(5), 754; https://doi.org/10.3390/jmse12050754 - 30 Apr 2024
Cited by 2 | Viewed by 1168
Abstract
Ship dynamic models serve as the foundation for designing ship controllers, trajectory planning, and obstacle avoidance. Support vector regression (SVR) is a commonly used nonparametric modelling method for ship dynamics. Achieving high accuracy SVR models requires a substantial amount of training samples. Additionally, [...] Read more.
Ship dynamic models serve as the foundation for designing ship controllers, trajectory planning, and obstacle avoidance. Support vector regression (SVR) is a commonly used nonparametric modelling method for ship dynamics. Achieving high accuracy SVR models requires a substantial amount of training samples. Additionally, as the number of training samples increases, the computational efficiency for solving the quadratic programming problem (QPP) of SVR decreases. Ship controllers demand dynamic models with both high accuracy and computational efficiency. Therefore, to enhance the prediction accuracy and computational efficiency of SVR, this paper proposes a nonparametric modelling method based on twin SVR (TSVR). TSVR replaces a large QPP with a set of smaller QPPs, significantly enhancing generalizability and computational efficiency. To further improve the predictive accuracy of TSVR, the puma optimizer algorithm is employed to determine the optimal hyperparameters. The performance of the proposed method is validated using a Mariner class vessel. Gaussian white noise is introduced into the modelling data to simulate measurement error. The TSVR model accurately predicts various zigzag and turning circle manoeuvring motions under disturbance conditions, demonstrating its robustness and generalizability. Compared to the SVR model, the TSVR model achieves lower root mean square error and computational time, confirming its superior predictive accuracy and computational efficiency. Full article
(This article belongs to the Special Issue Earth System Modeling, Data Assimilation, Artificial Intelligence, Deep Learning and Ocean Information Engineering II)
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23 pages, 3991 KiB  
Article
Maximum Impacts of the Initial and Model Parametric Errors on El Niño Predictions
by Lingjiang Tao
J. Mar. Sci. Eng. 2024, 12(4), 601; https://doi.org/10.3390/jmse12040601 - 30 Mar 2024
Viewed by 890
Abstract
With an El Niño prediction model, an advanced approach of conditional nonlinear optimal perturbation (CNOP) is used to reveal the maximum impacts of the errors occurring in initial conditions (ICs) and model parameters (MPs) on the El Niño predictions. The optimally growing initial [...] Read more.
With an El Niño prediction model, an advanced approach of conditional nonlinear optimal perturbation (CNOP) is used to reveal the maximum impacts of the errors occurring in initial conditions (ICs) and model parameters (MPs) on the El Niño predictions. The optimally growing initial errors CNOP-I and parameter errors CNOP-P are obtained, as well as their optimally combined mode (denoted by CNOPs). The comparisons among CNOP-I, -P, and CNOPs show that the El Niño predictions are more sensitive to the uncertainties in the MPs than in the ICs. The CNOP-I mainly affects the short-term prediction (less than 3 months), whereas the CNOP-P tends to induce much larger error over a longer prediction time. Both CNOP-I and CNOP-P can induce larger error growth during spring than during other seasons; that is to say, both of them cause the “spring predictability barrier” (SPB) phenomenon. The spring error growth caused by CNOP-I is mainly attributed to the uncertainties of the ocean advection processes, while that caused by the CNOP-P is controlled by thermodynamics. When the errors in ICs and MPs are simultaneously included in predictions, the resultant CNOPs produce much larger error growth and cause much more significant SPB; furthermore, the corresponding mechanism is dominated by the nonlinear advection processes. This certainly indicates that strong nonlinear interactions between the errors in ICs and MPs enhance the SPB, thus deepening our understanding of El Niño predictability. It is obvious that initial and model errors should be simultaneously given great attention to improve the El Niño prediction level. Full article
(This article belongs to the Special Issue Earth System Modeling, Data Assimilation, Artificial Intelligence, Deep Learning and Ocean Information Engineering II)
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20 pages, 7090 KiB  
Article
A Deep Neural Network-Ensemble Adjustment Kalman Filter and Its Application on Strongly Coupled Data Assimilation
by Renxi Wang and Zheqi Shen
J. Mar. Sci. Eng. 2024, 12(1), 108; https://doi.org/10.3390/jmse12010108 - 6 Jan 2024
Viewed by 1849
Abstract
This paper introduces a novel ensemble adjustment Kalman filter (EAKF) that integrates a machine-learning approach. The conventional EAKF adopts linear and Gaussian assumptions, making it difficult to handle cross-component updates in strongly coupled data assimilation (SCDA). The new approach employs nonlinear variable relationships [...] Read more.
This paper introduces a novel ensemble adjustment Kalman filter (EAKF) that integrates a machine-learning approach. The conventional EAKF adopts linear and Gaussian assumptions, making it difficult to handle cross-component updates in strongly coupled data assimilation (SCDA). The new approach employs nonlinear variable relationships established by a deep neural network (DNN) during the analysis stage of the EAKF, which nonlinearly projects observation increments into the state variable space. It can diminish errors in estimating cross-component error covariance arising from insufficient ensemble members, therefore improving the SCDA analysis. A conceptual coupled model is employed in this paper to conduct twin experiments, validating the DNN–EAKF’s capability to outperform conventional EAKF in SCDA. The results reveal that the DNN–EAKF can make SCDA superior to WCDA with a limited ensemble size. The root-mean-squared errors are reduced up to 70% while the anomaly correlation coefficients are increased up to 20% when the atmospheric observations are used to update the ocean variables directly. The other model components can also be improved through SCDA. This approach is anticipated to offer insights for future methodological integrations of machine learning and data assimilation and provide methods for SCDA applications in coupled general circulation models. Full article
(This article belongs to the Special Issue Earth System Modeling, Data Assimilation, Artificial Intelligence, Deep Learning and Ocean Information Engineering II)
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14 pages, 8364 KiB  
Article
A Two-Stage Network Based on Transformer and Physical Model for Single Underwater Image Enhancement
by Yuhao Zhang, Dujing Chen, Yanyan Zhang, Meiling Shen and Weiyu Zhao
J. Mar. Sci. Eng. 2023, 11(4), 787; https://doi.org/10.3390/jmse11040787 - 5 Apr 2023
Cited by 9 | Viewed by 2121
Abstract
The absorption and scattering properties of water can cause various distortions in underwater images, which limit the ability to investigate underwater resources. In this paper, we propose a two-stage network called WaterFormer to address this issue using deep learning and an underwater physical [...] Read more.
The absorption and scattering properties of water can cause various distortions in underwater images, which limit the ability to investigate underwater resources. In this paper, we propose a two-stage network called WaterFormer to address this issue using deep learning and an underwater physical imaging model. The first stage of WaterFormer uses the Soft Reconstruction Network (SRN) to reconstruct underwater images based on the Jaffe–McGramery model, while the second stage uses the Hard Enhancement Network (HEN) to estimate the global residual between the original image and the reconstructed result to further enhance the images. To capture long dependencies between pixels, we designed the encoder and decoder of WaterFormer using the Transformer structure. Additionally, we propose the Locally Intended Multiple Layer Perceptron (LIMP) to help the network process local information more effectively, considering the significance of adjacent pixels in enhancing distorted underwater images. We also proposed the Channel-Wise Self-Attention module (CSA) to help the network learn more details of the distorted underwater images by considering the correlated and different distortions in RGB channels. To overcome the drawbacks of physical underwater image enhancement (UIE) methods, where extra errors are introduced when estimating multiple physical parameters separately, we proposed the Joint Parameter Estimation method (JPE). In this method, we integrated multiple parameters in the Jaffe–McGramery model into one joint parameter (JP) through a special mathematical transform, which allowed for physical reconstruction based on the joint parameter (JP). Our experimental results show that WaterFormer can effectively restore the color and texture details of underwater images in various underwater scenes with stable performance. Full article
(This article belongs to the Special Issue Earth System Modeling, Data Assimilation, Artificial Intelligence, Deep Learning and Ocean Information Engineering II)
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