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Coastal Flood Hazard Risk Assessment and Mitigation Strategies

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water and Climate Change".

Deadline for manuscript submissions: 20 January 2026 | Viewed by 230

Special Issue Editors


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Guest Editor
Canadian Centre for Climate Change and Adaptation, University of Prince Edward Island, St. Peters Bay, Charlottetown, PE C0A 2A0, Canada
Interests: hydrological cycle; water resources planning and management; remote sensing; artificial intelligence; climate change adaptation; irrigation water management

Special Issue Information

Dear Colleagues,

Rising sea levels and increasingly frequent extreme weather events, combined with the rapid growth of coastal populations, emphasize the urgent need for advanced risk assessment and mitigation in coastal and low-lying areas.

For this Special Issue, we welcome high-quality, collaborative research on the impacts of climate change and coastal processes, aimed at strengthening the resilience of vulnerable coastal regions. Original research, case studies, and reviews that advance knowledge in climate risk, vulnerability, and adaptation are encouraged for submission.

Topics of interest include, but are not limited to, the following:

  • Coastal flooding under present and future climate scenarios;
  • Hydrological and hydrodynamical modeling;
  • GIS and remote sensing-based hazard assessment and mapping;
  • Nature-based and engineered coastal protection measures;
  • Socioeconomic impacts and community-level adaptation strategies;
  • Integrated risk management and policy frameworks for resilience building.

We welcome submissions in the form of research articles, reviews, brief communications, case studies, commentaries, registered reports, opinions, and data reports.

Dr. Quan Van Dau
Prof. Dr. Xander Wang
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. Water 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

  • climate change
  • water security
  • hydrological modeling
  • coastal erosion
  • climate adaptation
  • compound flooding
  • sea level rise
  • extreme weather event

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Published Papers (1 paper)

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Research

21 pages, 979 KiB  
Article
AI-Enhanced Coastal Flood Risk Assessment: A Real-Time Web Platform with Multi-Source Integration and Chesapeake Bay Case Study
by Paul Magoulick
Water 2025, 17(15), 2231; https://doi.org/10.3390/w17152231 - 26 Jul 2025
Viewed by 163
Abstract
A critical gap exists between coastal communities’ need for accessible flood risk assessment tools and the availability of sophisticated modeling, which remains limited by technical barriers and computational demands. This study introduces three key innovations through Coastal Defense Pro: (1) the first operational [...] Read more.
A critical gap exists between coastal communities’ need for accessible flood risk assessment tools and the availability of sophisticated modeling, which remains limited by technical barriers and computational demands. This study introduces three key innovations through Coastal Defense Pro: (1) the first operational web-based AI ensemble for coastal flood risk assessment integrating real-time multi-agency data, (2) an automated regional calibration system that corrects systematic model biases through machine learning, and (3) browser-accessible implementation of research-grade modeling previously requiring specialized computational resources. The system combines Bayesian neural networks with optional LSTM and attention-based models, implementing automatic regional calibration and multi-source elevation consensus through a modular Python architecture. Real-time API integration achieves >99% system uptime with sub-3-second response times via intelligent caching. Validation against Hurricane Isabel (2003) demonstrates correction from 197% overprediction (6.92 m predicted vs. 2.33 m observed) to accurate prediction through automated identification of a Chesapeake Bay-specific reduction factor of 0.337. Comprehensive validation against 15 major storms (1992–2024) shows substantial improvement over standard methods (RMSE = 0.436 m vs. 2.267 m; R2 = 0.934 vs. −0.786). Economic assessment using NACCS fragility curves demonstrates 12.7-year payback periods for flood protection investments. The open-source Streamlit implementation democratizes access to research-grade risk assessment, transforming months-long specialist analyses into immediate browser-based tools without compromising scientific rigor. Full article
(This article belongs to the Special Issue Coastal Flood Hazard Risk Assessment and Mitigation Strategies)
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