A Review of Machine Learning Applications to Coastal Sediment Transport and Morphodynamics

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1016/j.earscirev.2019.04.022. This is version 3 of this Preprint.


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Evan B Goldstein , Giovanni Coco, Nathaniel G. Plant


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.




Earth Sciences, Geomorphology, Physical Sciences and Mathematics


machine learning, coastal morphodynamics, coastal sediment transport, data-driven science


Published: 2018-08-23 14:16

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CC BY Attribution 4.0 International

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