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Machine-Learning Methods and Tools in Coastal and Ocean Engineering
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Machine-Learning Methods and Tools in Coastal and Ocean Engineering

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

Deadline for manuscript submissions: closed (10 February 2021) | Viewed by 13206

Special Issue Editors

Prof. Dr. Jose E. Castillo

E-Mail Website
Guest Editor
Computational Science Research Center, San Diego State University, 5500 Campanile Dr, San Diego, CA 92182-1245, USA
Interests: coastal ocean dynamics; computational modeling and scientific computing
Dr. Sara Mizar Formentin

E-Mail Website
Guest Editor
Department of Civil, Chemical, Environmental and Materials Engineering, University of Bologna, Viale del Risorgimento 2, 40136 Bologna, Italy
Interests: coastal engineering; coastal structures; numerical modelling; machine learning

Special Issue Information

Dear Colleagues,

The research on and interest in machine-learning methods for Coastal and Ocean applications is increasing rapidly due to their versatility, efficiency and accuracy. Successful examples of machine-learning tools (Artificial or Fuzzy Neural Networks, Support Vector Machine) have been developed for the prediction of seawater level, wave forecasting, assessment of structural stability, prediction of the scour and erosion of marine structures, assessment of the hydraulic performance of coastal and harbor structures, and analysis of wave-structure interaction processes. The literature dedicated to these studies show that the machine-learning approach achieves an improved performance compared to traditional formulae and ensures a significant reduction in the computational effort and the machine time execution in comparison to numerical modelling.

This Special Issue invites authors to submit original articles dedicated to innovative applications of machine learning methods in Coastal and Ocean engineering. These articles can be focused i) on demonstrating innovative solutions to reduce the parameter count and amount of training data (Extreme Learning Methods), simplifying the training step and shortening the computational time; ii) on adaptive machine-learning methods (Genetic Algorithm), to build up analytical formulae; iii) on innovative and advanced applications, such as optimization problems (e.g. the Harmony Search Algorithm for the design of coastal structures), pattern recognition (e.g., for the automatic detection of the free-surface, the estimation of the amount of air entrainment in bi-phase flows, the set-up of alarm systems based on video-monitoring), or cost minimization (e.g., for the setup of decision support systems).

Prof. Dr. Jose E. Castillo
Dr. Sara Mizar Formentin
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

  • machine learning
  • artificial neural networks
  • artificial intelligence
  • coastal ocean engineering
  • coastal ocean dynamics
  • coastal management

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Published Papers (3 papers)

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Research

22 pages, 4551 KiB  
Article
Modeling of Ship Fuel Consumption Based on Multisource and Heterogeneous Data: Case Study of Passenger Ship
by Yongjie Zhu, Yi Zuo and Tieshan Li
J. Mar. Sci. Eng. 2021, 9(3), 273; https://doi.org/10.3390/jmse9030273 - 3 Mar 2021
Cited by 23 | Viewed by 3692
Abstract
In the current shipping industry, quantitative measures of ship fuel consumption (SFC) have become one of the most important research topics in environmental protection and energy management related to shipping operations. In particular, the rapid development of sensor technologies enables multisource data collection [...] Read more.
In the current shipping industry, quantitative measures of ship fuel consumption (SFC) have become one of the most important research topics in environmental protection and energy management related to shipping operations. In particular, the rapid development of sensor technologies enables multisource data collection to improve the modeling of the SFC problem. To address the features of such heterogeneous data, this paper proposes an integrated model for the estimation of SFC that includes three modules: a multisource data collection module, a heterogeneous data feature fusion module and a fuel consumption estimation module. First, in the data collection module, data related to SFC are collected by multiple sensors installed aboard the ship. Second, the feature fusion module employs a series of moving overlapped frames to merge different frequency data into small frames so that fusion features can be extracted from the heterogeneous data of multiple sources. Finally, in the fuel estimation module, the fusion features provide a novel way to consider the modeling and estimation of SFC as a classical time-series analysis using various machine learning techniques. Experimentally, linear regression (LR), support vector regression (SVR), and artificial neural network (ANN) were employed as the machine learning methods to train SFC models. Compared with the traditional feature extraction method, the accuracy of LR, SVR, and ANN were improved by 8.5, 0.35 and 51.5%, respectively, using the proposed method. The main contribution of this work is to consider the multisource and heterogeneous problem of sensor-based SFC data and propose an integrated model to extract the information of SFC data. Moreover, the experimental results showed that the estimation accuracy can be greatly improved. Full article
(This article belongs to the Special Issue Machine-Learning Methods and Tools in Coastal and Ocean Engineering)
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16 pages, 5801 KiB  
Article
Machine Learning Based Predictions of Dissolved Oxygen in a Small Coastal Embayment
by Manuel Valera, Ryan K. Walter, Barbara A. Bailey and Jose E. Castillo
J. Mar. Sci. Eng. 2020, 8(12), 1007; https://doi.org/10.3390/jmse8121007 - 9 Dec 2020
Cited by 18 | Viewed by 3428
Abstract
Coastal dissolved oxygen (DO) concentrations have a profound impact on nearshore ecosystems and, in recent years, there has been an increased prevalance of low DO hypoxic events that negatively impact nearshore organisms. Even with advanced numerical models, accurate prediction of coastal DO variability [...] Read more.
Coastal dissolved oxygen (DO) concentrations have a profound impact on nearshore ecosystems and, in recent years, there has been an increased prevalance of low DO hypoxic events that negatively impact nearshore organisms. Even with advanced numerical models, accurate prediction of coastal DO variability is challenging and computationally expensive. Here, we apply machine learning techniques in order to reconstruct and predict nearshore DO concentrations in a small coastal embayment while using a comprehensive set of nearshore and offshore measurements and easily measured input (training) parameters. We show that both random forest regression (RFR) and support vector regression (SVR) models accurately reproduce both the offshore DO and nearshore DO with extremely high accuracy. In general, RFR consistently peformed slightly better than SVR, the latter of which was more difficult to tune and took longer to train. Although each of the nearshore datasets were able to accurately predict DO values using training data from the same site, the model only had moderate success when using training data from one site to predict DO at another site, which was likely due to the the complexities in the underlying dynamics across the sites. We also show that high accuracy can be achieved with relatively little training data, highlighting a potential application for correcting time series with missing DO data due to quality control or sensor issues. This work establishes the ability of machine learning models to accurately reproduce DO concentrations in both offshore and nearshore coastal waters, with important implications for the ability to detect and indirectly measure coastal hypoxic events in near real-time. Future work should explore the ability of machine learning models in order to accurately forecast hypoxic events. Full article
(This article belongs to the Special Issue Machine-Learning Methods and Tools in Coastal and Ocean Engineering)
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23 pages, 6026 KiB  
Article
Determination of Semi-Empirical Models for Mean Wave Overtopping Using an Evolutionary Polynomial Paradigm
by Corrado Altomare, Daniele B. Laucelli, Hajime Mase and Xavi Gironella
J. Mar. Sci. Eng. 2020, 8(8), 570; https://doi.org/10.3390/jmse8080570 - 29 Jul 2020
Cited by 13 | Viewed by 3687
Abstract
The present work employs the so-called Evolutionary Polynomial Regression (EPR) algorithm to build up a formula for the assessment of mean wave overtopping discharge for smooth sea dikes and vertical walls. EPR is a data-mining tool that combines and integrates numerical regression and [...] Read more.
The present work employs the so-called Evolutionary Polynomial Regression (EPR) algorithm to build up a formula for the assessment of mean wave overtopping discharge for smooth sea dikes and vertical walls. EPR is a data-mining tool that combines and integrates numerical regression and genetic programming. This technique is here employed to dig into the relationship between the mean discharge and main hydraulic and structural parameters that characterize the problem under study. The parameters are chosen based on the existing and most used semi-empirical formulas for wave overtopping assessment. Besides the structural freeboard or local wave height, the unified models highlight the importance of local water depth and wave period in combination with foreshore slope and dike slope on the overtopping phenomena, which are combined in a unique parameter that is defined either as equivalent or imaginary slope. The obtained models aim to represent a trade-off between accuracy and parsimony. The final formula is simple but can be employed for a preliminary assessment of overtopping rates, covering the full range of dike slopes, from mild to vertical walls, and of water depths from the shoreline to deep water, including structures with emergent toes. Full article
(This article belongs to the Special Issue Machine-Learning Methods and Tools in Coastal and Ocean Engineering)
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