利用离岸波浪反射光谱的机器学习预测海滩剖面

IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Elsa Disdier , Rafael Almar , Rachid Benshila , Mahmoud Al Najar , Romain Chassagne , Debajoy Mukherjee , Dennis G. Wilson
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引用次数: 0

摘要

跟踪和预报沿岸形态的变化,对开发、降低风险和整个沿岸管理至关重要。当前沿岸研究和工程学面临的一个挑战,是找到一种能够准确评估沿岸水深剖面以及 斜坡和沙洲等关键参数的方法。传统的水深测量是通过回声探测获得的,这种方法费时、危险、成本高。利用各种模拟案例,我们测试了机器学习,特别是神经网络的潜力,以便根据岸基波浪反射,从离岸感应波浪中重建海岸水深剖面。可以捕捉前滩坡度、曲率、沙洲振幅和位置等特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting beach profiles with machine learning from offshore wave reflection spectra
Tracking and forecasting changes in coastal morphology is vital for development, risk reduction, and overall coastal management. One challenge of current coastal research and engineering is to find a method able to accurately assess the bathymetry profile along the coast and key parameters such as slope and sandbars. Traditional bathymetry measurements are obtained through echo-sounding, which is time-consuming, hazardous and costly. Using a variety of simulated cases, we test the potential of machine learning and in particular Neural Networks to reconstruct the coastal bathymetry profile from offshore sensed waves, based on shore-based wave reflection. Features such as foreshore slope, curvature, sandbars amplitude and positions can be captured.
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来源期刊
Environmental Modelling & Software
Environmental Modelling & Software 工程技术-工程:环境
CiteScore
9.30
自引率
8.20%
发文量
241
审稿时长
60 days
期刊介绍: Environmental Modelling & Software publishes contributions, in the form of research articles, reviews and short communications, on recent advances in environmental modelling and/or software. The aim is to improve our capacity to represent, understand, predict or manage the behaviour of environmental systems at all practical scales, and to communicate those improvements to a wide scientific and professional audience.
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