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Coastal Disaster Assessment and Response—2nd Edition
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Coastal Disaster Assessment and Response—2nd Edition

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: 20 August 2026 | Viewed by 3401

Special Issue Editor

Dr. Deniz Velioglu Sogut

E-Mail Website
Guest Editor
Ocean Engineering and Marine Sciences, Florida Institute of Technology, 150 W University Blvd., Melbourne, FL 32901, USA
Interests: coastal resilience; coastal hazards; nearshore processes; nature-based solutions; storm surge; tsunami; wave flume experiments; data analysis and processing; high-performance computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Journal of Marine Science and Engineering is delighted to introduce the Special Issue, “Coastal Disaster Assessment and Response—2nd Edition”, building on the success of the previous edition with the same title.

Over the past decades, coastal hazards have significantly increased in both intensity and frequency, posing escalating risks to communities, infrastructure, and ecosystems. Given the high population densities in low-elevation coastal zones, the urgency to enhance disaster preparedness, response, and long-term sustainability has never been greater. A key challenge lies in developing adaptive mitigation strategies that not only address immediate disaster response but also foster resilient coastal communities.

This Special Issue seeks to explore innovative approaches to disaster risk reduction, emergency preparedness, and recovery efforts tailored to the unique vulnerabilities of coastal regions. We invite contributions that examine cutting-edge methodologies for risk assessment, real-time disaster response, post-disaster reconstruction, and proactive mitigation measures that integrate environmental and technological considerations to advance in sustainable coastal development.

We welcome original studies employing diverse methodologies, including analytical modeling, numerical simulations, experimental investigations, and field-based assessments, to enhance our understanding of coastal disaster dynamics. Interdisciplinary perspectives that bridge science, engineering, and community engagement are highly valued. By fostering knowledge exchange, this Special Issue aims to contribute to more effective disaster management practices and sustainable solutions that safeguard coastal ecosystems against future hazards.

We look forward to receiving your contributions.

Dr. Deniz Velioglu Sogut
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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 semimonthly 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

  • hurricane
  • storm surge
  • tsunami
  • nature-based solutions
  • vegetation
  • coastal resilience
  • mitigation

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Related Special Issue

Published Papers (4 papers)

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Research

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21 pages, 2215 KB  
Article
Machine Learning Approaches for Probabilistic Prediction of Coastal Freak Waves
by Dong-Jiing Doong, Wei-Cheng Chen, Fan-Ju Lin, Chi Pan and Cheng-Han Tsai
J. Mar. Sci. Eng. 2026, 14(8), 689; https://doi.org/10.3390/jmse14080689 - 8 Apr 2026
Viewed by 391
Abstract
Coastal freak waves (CFWs) are sudden and hazardous wave events that occur near shorelines and can pose serious threats to coastal visitors and infrastructure. Due to the complex interactions among coastal bathymetry, wave dynamics, and environmental conditions, the mechanisms governing CFW formation remain [...] Read more.
Coastal freak waves (CFWs) are sudden and hazardous wave events that occur near shorelines and can pose serious threats to coastal visitors and infrastructure. Due to the complex interactions among coastal bathymetry, wave dynamics, and environmental conditions, the mechanisms governing CFW formation remain poorly understood, making reliable prediction difficult. This study investigates the feasibility of applying machine learning techniques to predict CFW occurrences using observational environmental data. Three machine learning algorithms, the Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN), were developed to generate probability-based predictions of CFW events. Environmental variables derived from buoy observations, including wave characteristics, wind conditions, swell parameters, wave grouping indicators, and nonlinear wave interaction indices, were used as model inputs. Hyperparameters were optimized using grid search combined with k-fold cross-validation. The results show that all three models achieved comparable predictive performance, with AUC values close to 0.80 and overall prediction accuracy around 74%. The ANN model achieved the highest recall, indicating strong capability in detecting CFW events, while the RF and SVM models showed more balanced precision and recall. Analysis of high-probability prediction events suggests that CFW occurrences are associated with swell-dominated conditions, strong wave grouping behavior, and enhanced nonlinear wave interactions. These results demonstrate that machine learning provides a promising framework for probabilistic prediction of coastal freak waves and has potential applications in coastal hazard assessment and early warning systems. Full article
(This article belongs to the Special Issue Coastal Disaster Assessment and Response—2nd Edition)
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20 pages, 15337 KB  
Article
Stability of Beach Nourishment Under Extreme Wave Conditions: Insights from Physical-Model Experiments and XBeach Simulations
by Tingting Zhu, Bo Hu, Hao Wang, Hanbao Chen, Baolei Geng, Longzai Ge and Ruijia Jin
J. Mar. Sci. Eng. 2026, 14(7), 613; https://doi.org/10.3390/jmse14070613 - 26 Mar 2026
Viewed by 478
Abstract
Beach nourishment is a widely adopted nature-based solution for coastal erosion; however, its design efficacy and morphodynamic resilience under extreme wave conditions remain inadequately quantified, posing challenges for coastal hazard assessment. This study integrates physical-model experiments and XBeach numerical simulations to investigate the [...] Read more.
Beach nourishment is a widely adopted nature-based solution for coastal erosion; however, its design efficacy and morphodynamic resilience under extreme wave conditions remain inadequately quantified, posing challenges for coastal hazard assessment. This study integrates physical-model experiments and XBeach numerical simulations to investigate the hydrodynamic and morphodynamic behavior of nourished beaches subjected to typhoon-driven extreme wave conditions at a headland-bay beach on Meizhou Island, China. Physical-model experiments were conducted to examine shoreline response and sediment redistribution under extreme waves for three nourishment tests. XBeach simulations resolved wave-induced currents, water-level variations, and sediment transport processes, enabling continuous tracking of nearshore hydrodynamics and beach profile evolution for three nourishment tests during Typhoon Doksuri. Results indicate that nourishment geometry and groin configuration play a dominant role in wave breaking patterns, sediment transport pathways and erosion–deposition distributions. Groin positions strongly influence alongshore sediment transport. Relocating the groin to an accretional zone reduces lee-side erosion and promotes a more stable shoreline. Steeper nourishment foreshore slopes promote offshore wave shoaling and breaking, enhancing fast wave-energy dissipation, shifting erosion seaward and limiting landward erosion extent. Consistent responses from both experimental and numerical results demonstrate that nourishment stability under extreme wave conditions is better characterized by the combined effects of erosion extent, erosion length, erosion depth, erosion volume, and alongshore and cross-shore sediment redistribution. The integrated physical–numerical approach provides a practical framework for assessing beach nourishment stability during coastal hazard events and offers guidance for the design and evaluation of resilient beach nourishment in wave-dominated, typhoon-prone coastal regions. Full article
(This article belongs to the Special Issue Coastal Disaster Assessment and Response—2nd Edition)
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25 pages, 13109 KB  
Article
Interpretation Analysis of Influential Variables Dominating Impulse Waves Generated by Landslides
by Xiaohan Xu, Peng Qin, Zhenyu Li, Jiangfei Wang, Yuyue Zhou, Sen Zheng and Zhenzhu Meng
J. Mar. Sci. Eng. 2025, 13(12), 2223; https://doi.org/10.3390/jmse13122223 - 21 Nov 2025
Viewed by 663
Abstract
Landslide impacts into water generate impulse waves that, in confined basins and along steep coasts, escalate swiftly into hazardous near-shore surges. In this study, we present a scenario-aware workflow using gradient boosting and k-means clustering, and explain them using Shapley additive explanations [...] Read more.
Landslide impacts into water generate impulse waves that, in confined basins and along steep coasts, escalate swiftly into hazardous near-shore surges. In this study, we present a scenario-aware workflow using gradient boosting and k-means clustering, and explain them using Shapley additive explanations (SHAPs). Two cases are addressed: forecasting at water entry (Scenario I) with predictors Froude number Fr, relative effective mass M, and relative thickness S; and pre-event assessment (Scenario II) with predictors Bingham number Bi, relative moving length L, and relative initial mass Mi. Using 270 controlled physical-model experiments, we benchmark six learning algorithms under 5-fold cross-validation. Gradient boosting delivers the best overall accuracy and cross-scenario robustness, with XGBoost close behind. Scenario I attains a coefficient of determination R2 of 0.941, while Scenario II achieves R2=0.865. Residual analyses indicate narrower spreads and lighter tails for the top models. SHAP reveals physics-consistent controls: M and Fr dominate Scenario I, whereas initial mass and the Bi dominate Scenario II; interactions Fr×S and Mi×Bi clarify non-linear amplification of wave amplitude and height. The cluster–predict–explain framework couples predictive skill with physical transparency and is directly applicable to coastal hazard screening and integration into shoreline early-warning workflows. Full article
(This article belongs to the Special Issue Coastal Disaster Assessment and Response—2nd Edition)
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Review

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31 pages, 2685 KB  
Review
Predicting Coastal Flooding and Overtopping with Machine Learning: Review and Future Prospects
by Moeketsi L. Duiker, Victor Ramos, Francisco Taveira-Pinto and Paulo Rosa-Santos
J. Mar. Sci. Eng. 2025, 13(12), 2384; https://doi.org/10.3390/jmse13122384 - 16 Dec 2025
Cited by 1 | Viewed by 1046
Abstract
Flooding and overtopping are major concerns in coastal areas due to their potential to cause severe damage to infrastructure, economic activities, and human lives. Traditional methods for predicting these phenomena include numerical and physical models, as well as empirical formulations. However, these methods [...] Read more.
Flooding and overtopping are major concerns in coastal areas due to their potential to cause severe damage to infrastructure, economic activities, and human lives. Traditional methods for predicting these phenomena include numerical and physical models, as well as empirical formulations. However, these methods have limitations, such as the high computational costs, reliance on extensive field data, and reduced accuracy under complex conditions. Recent advances in machine learning (ML) offer new opportunities to improve predictive capabilities in coastal engineering. This paper reviews ML applications for coastal flooding and overtopping prediction, analyzing commonly used models, data sources, and preprocessing techniques. Several studies report that ML models can match or exceed the performance of traditional approaches, such as empirical EurOtop formulas or high-fidelity numerical models, particularly in controlled laboratory datasets where numerical models are computationally intensive and empirical methods show larger estimation errors. However, their advantages remain task- and data-dependent, and their generalization and interpretability may lag behind physics-based methods. This review also examines recent developments, such as hybrid approaches, real-time monitoring, and explainable artificial intelligence, which show promise in addressing these limitations and advancing the operational use of ML in coastal flooding and overtopping prediction. Full article
(This article belongs to the Special Issue Coastal Disaster Assessment and Response—2nd Edition)
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