DOI: 10.1016/j.earscirev.2019.04.022
论文题名: A review of machine learning applications to coastal sediment transport and morphodynamics
作者: Goldstein E.B. ; Coco G. ; Plant N.G.
刊名: Earth Science Reviews
ISSN: 00128252
出版年: 2019
卷: 194 起始页码: 97
结束页码: 108
语种: 英语
英文关键词: coastal morphology
; coastal sediment
; machine learning
; morphodynamics
; prediction
; regression analysis
; sediment transport
英文摘要: A range of computer science methods termed machine learning (ML)enables the extraction of insight and quantitative relationships from multidimensional datasets. Here, we review the use of ML on supervised regression tasks in studies of coastal morphodynamics and sediment transport. We examine aspects of ‘what’ and ‘why’, such as ‘what’ science problems ML tools have been used to address, ‘what’ was learned when using ML, and ‘why’ authors used ML methods. We find a variety of research questions have been addressed, ranging from small-scale predictions of sediment transport to larger-scale sand bar morphodynamics and coastal overwash on a developed island. We find various reasons justify the use of ML, including maximize predictability, emulation of model components, the need for smooth and continuous nonlinear regression, and explicit inclusion of uncertainty. The expanding use of ML has allowed for an expanding set of questions to be addressed. After reviewing the studies we outline a set of best practices for coastal researchers using machine learning methods. Finally we suggest possible areas for future research, including the use of novel machine learning techniques and exploring open data that is becoming increasingly available. © 2019 Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/165815
Appears in Collections: 气候变化与战略
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作者单位: Department of Geography, Environment, and Sustainability, University of North Carolina at Greensboro, Graham Building, 1009 Spring Garden St., Greensboro, NC 27412, United States; School of Environment, University of Auckland, New Zealand; U.S. Geological Survey St. Petersburg Coastal and Marine Science Center, St. Petersburg, FL, United States
Recommended Citation:
Goldstein E.B.,Coco G.,Plant N.G.. A review of machine learning applications to coastal sediment transport and morphodynamics[J]. Earth Science Reviews,2019-01-01,194