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JMSE
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Coastal Wave Prediction and Analysis Using Machine Learning
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Coastal Wave Prediction and Analysis Using Machine Learning

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

Deadline for manuscript submissions: closed (30 May 2024) | Viewed by 788

Special Issue Editors

Dr. Taeyoon Kim

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, North Carolina State University, Raleigh, NC, USA
Interests: submerged breakwater; artificial coral reef; wave attenuation; return flow; coastal erosion
Dr. Woo-Dong Lee

E-Mail Website
Guest Editor
Department of Ocean Civil Engineering, Gyeongsang National University, Tongyeong, Republic of Korea
Interests: machine learning; numerical modeling; coastal processes; sediment transport; wave hydrodynamics

Special Issue Information

Dear Colleagues,

(1) Introduction: We are pleased to invite you to contribute to our Special Issue on "Coastal Wave Prediction and Analysis Using Machine Learning". With the increasing significance of accurate wave forecasts for maritime operations, coastal management, and environmental monitoring, this collection aims to showcase innovative research in machine learning applications within the context of coastal wave dynamics.

(2) Aim of the Special Issue: This Special Issue aims to bring together cutting-edge research at the intersection of coastal dynamics and machine learning, collating at least 10 high-quality articles that delve into advancements in predictive models, data-driven approaches, and interdisciplinary collaborations within the realm of coastal wave prediction and analysis. The Special Issue may be compiled into book form if the submission target is met.

(3) Suggested Themes and Article Types for Submissions: Original research articles and reviews are welcome in areas such as coastal wave prediction, interpretable machine learning, surrogate modeling of fluid dynamics, oceanographic data analysis, remote sensing, wave energy forecasting, data-driven coastal management, interdisciplinary approaches, maritime operations, and environmental monitoring.

Dr. Taeyoon Kim
Dr. Woo-Dong Lee
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

  • coastal wave prediction
  • interpretable machine learning models
  • surrogate modeling of fluid dynamics
  • oceanographic data analysis
  • remote sensing
  • wave energy forecasting
  • data-driven coastal management
  • interdisciplinary approaches
  • maritime operations
  • environmental monitoring

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

Published Papers (1 paper)

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Research

13 pages, 4247 KiB  
Article
Prediction Analysis of Sea Level Change in the China Adjacent Seas Based on Singular Spectrum Analysis and Long Short-Term Memory Network
by Yidong Xie, Shijian Zhou and Fengwei Wang
J. Mar. Sci. Eng. 2024, 12(8), 1397; https://doi.org/10.3390/jmse12081397 - 15 Aug 2024
Viewed by 497
Abstract
Considering the nonlinear and non-stationary characteristics of sea-level-change time series, this study focuses on enhancing the predictive accuracy of sea level change. The adjacent seas of China are selected as the research area, and the study integrates singular spectrum analysis (SSA) with long [...] Read more.
Considering the nonlinear and non-stationary characteristics of sea-level-change time series, this study focuses on enhancing the predictive accuracy of sea level change. The adjacent seas of China are selected as the research area, and the study integrates singular spectrum analysis (SSA) with long short-term memory (LSTM) neural networks to establish an SSA-LSTM hybrid model for predicting sea level change based on sea level anomaly datasets from 1993 to 2021. Comparative analyses are conducted between the SSA-LSTM hybrid model and singular LSTM neural network model, as well as (empirical mode decomposition) EMD-LSTM and (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) CEEMDAN-LSTM hybrid models. Evaluation metrics, including the root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2), are employed for the accuracy assessment. The results demonstrate a significant improvement in prediction accuracy using the SSA-LSTM hybrid model, with an RMSE of 5.26 mm, MAE of 4.27 mm, and R2 of 0.98, all surpassing those of the other models. Therefore, it is reasonable to conclude that the SSA-LSTM hybrid model can more accurately predict sea level change. Full article
(This article belongs to the Special Issue Coastal Wave Prediction and Analysis Using Machine Learning)
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