Skip to main content
Prediction of wave ripple characteristics using genetic programming

Prediction of wave ripple characteristics using genetic programming

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1016/j.csr.2013.09.020. This is version 1 of this Preprint.

Add a Comment

You must log in to post a comment.


Comments

There are no comments or no comments have been made public for this article.

Downloads

Download Preprint

Supplementary Files

Authors

Evan B Goldstein , Giovanni Coco, A. Brad Murray

Abstract

We integrate published data sets of field and laboratory experiments of wave ripples and use genetic programming, a machine learning paradigm, in an attempt to develop a universal equilibrium predictor for ripple wavelength, height, and steepness. We train our genetic programming algorithm with data selected using a maximum dissimilarity selection routine. Thanks to this selection algorithm we use less data to train the genetic programming software, allowing more data to be used as testing (i.e. to compare our predictor vs. common prediction schemes). Our resulting predictor is smooth and physically meaningful, different from other machine le...  more

DOI

https://doi.org/10.31223/osf.io/duzq5

Subjects

Earth Sciences, Geomorphology, Physical Sciences and Mathematics

Keywords

machine learning, bedforms, Genetic Programming, Ripples

Dates

Published: 2018-01-22 01:29

License

CC BY Attribution 4.0 International