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A review of machine learning applications to coastal sediment transport and morphodynamics

Earth-Science Reviews
By: , , and 
https://doi.org/10.1016/j.earscirev.2019.04.022

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Abstract

A range of computer science methods under the heading of machine learning (ML) enables the extraction of insight and quantitative relationships from multidimensional datasets. Here, we review some common ML methods and their application to studies of coastal morphodynamics and sediment transport. We examine aspects of ‘what’ and ‘why’ ML methods contribute, 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, smooth and continuous nonlinear regression through data, and explicit inclusion of uncertainty. Overall 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.
Publication type Article
Publication Subtype Journal Article
Title A review of machine learning applications to coastal sediment transport and morphodynamics
Series title Earth-Science Reviews
DOI 10.1016/j.earscirev.2019.04.022
Volume 194
Year Published 2019
Language English
Publisher Elsevier
Contributing office(s) St. Petersburg Coastal and Marine Science Center
Description 11 p.
First page 97
Last page 108
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