Hybrid vegetation-seawall coastal systems for wave hazard reduction: analytics for cost-effective design from optimized features

Abstract Coastal areas, essential for human settlement and marine biodiversity, face persistent flood hazards. Integrating vegetation with traditional coastal defense structures, such as seawalls, offers a promising solution for robust and cost-effective flood mitigation. However, optimizing hybrid...

Full description

Saved in:
Bibliographic Details
Main Authors: Erfan Amini, Reza Marsooli, Somayeh Moazeni, Bilal M. Ayyub
Format: Article
Language:English
Published: Nature Portfolio 2025-03-01
Series:npj Natural Hazards
Online Access:https://doi.org/10.1038/s44304-025-00070-x
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Abstract Coastal areas, essential for human settlement and marine biodiversity, face persistent flood hazards. Integrating vegetation with traditional coastal defense structures, such as seawalls, offers a promising solution for robust and cost-effective flood mitigation. However, optimizing hybrid vegetation-seawall solutions to enhance coastal protection while addressing varying risk tolerances is a challenging task. This study develops a novel framework combining a non-hydrostatic wave model, a data-driven surrogate model, and a multi-objective optimization algorithm to optimize hybrid designs. Results demonstrate that vegetation integration significantly reduces wave impacts, enhancing seawall performance. Optimized designs reveal that higher vegetation area provides greater wave energy dissipation, while vegetation density plays a more nuanced role depending on available space and risk tolerance levels. For critical infrastructure with low-risk tolerance, designs emphasize seawall height and moderate vegetation density, whereas high-risk tolerance prioritizes larger vegetated areas with lower density. The developed framework equips decision-makers to design hybrid systems that balance coastal protection and cost-effectiveness based on their specific objectives and constraints.
ISSN:2948-2100