Machine learning-driven approach to quantify the beach susceptibility to storm-induced erosion

IF 1.9 3区工程技术 Q3 ENGINEERING, CIVIL

Coastal Engineering Journal Pub Date : 2023-12-17 DOI:10.1080/21664250.2023.2288744

Salika Thilakarathne, Takayuki Suzuki, Martin Mäll

引用次数: 0

Abstract

This study focuses on quantifying the susceptibility of sandy beaches to storm-induced erosion by analyzing 14 key morphometric indicators. We used a 24-year morphological and metocean dataset, en...

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用机器学习驱动的方法量化海滩易受风暴侵蚀的程度

本研究主要通过分析 14 个关键形态指标来量化沙滩易受风暴侵蚀的程度。我们使用了一个为期 24 年的形态和海洋数据集，并对这些数据进行了分析。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Coastal Engineering Journal 工程技术-工程：大洋

CiteScore

4.60

自引率

8.30%

发文量

审稿时长

7.5 months

期刊介绍： Coastal Engineering Journal is a peer-reviewed medium for the publication of research achievements and engineering practices in the fields of coastal, harbor and offshore engineering. The CEJ editors welcome original papers and comprehensive reviews on waves and currents, sediment motion and morphodynamics, as well as on structures and facilities. Reports on conceptual developments and predictive methods of environmental processes are also published. Topics also include hard and soft technologies related to coastal zone development, shore protection, and prevention or mitigation of coastal disasters. The journal is intended to cover not only fundamental studies on analytical models, numerical computation and laboratory experiments, but also results of field measurements and case studies of real projects.