气候变化领域集成服务门户(CCPortal): A review of machine learning applications to coastal sediment transport and morphodynamics

CCPortal

DOI	10.1016/j.earscirev.2019.04.022
	A review of machine learning applications to coastal sediment transport and morphodynamics
	Goldstein E.B.; Coco G.; Plant N.G.
发表日期	2019
ISSN	00128252
起始页码	97
结束页码	108
卷号	194
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
英文关键词	coastal morphology; coastal sediment; machine learning; morphodynamics; prediction; regression analysis; sediment transport
语种	英语
来源期刊	Earth Science Reviews
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/203438
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
推荐引用方式 GB/T 7714	Goldstein E.B.,Coco G.,Plant N.G.. A review of machine learning applications to coastal sediment transport and morphodynamics[J],2019,194.
APA	Goldstein E.B.,Coco G.,&Plant N.G..(2019).A review of machine learning applications to coastal sediment transport and morphodynamics.Earth Science Reviews,194.
MLA	Goldstein E.B.,et al."A review of machine learning applications to coastal sediment transport and morphodynamics".Earth Science Reviews 194(2019).