A machine learning approach to predicting equilibrium ripple wavelength

Environmental Modeling and Software
By: , and 



Sand ripples are geomorphic features on the seafloor that affect bottom boundary layer dynamics including wave attenuation and sediment transport. We present a new equilibrium ripple predictor using a machine learning approach that outputs a probability distribution of wave-generated equilibrium wavelengths and statistics including an estimate of ripple height, the most probable ripple wavelength, and sediment and flow parameterizations. The Bayesian Optimal Model System (BOMS) is an ensemble machine learning system that combines two machine learning algorithms and two deterministic empirical ripple predictors with a Bayesian meta-learner to produce probabilistic wave-generated equilibrium ripple wavelength estimates in sandy locations. A ten-fold cross validation of BOMS resulted in an adjusted R-squared value of 0.93 and an average root mean square error (RMSE) of 8.0 cm. During both cross validation and testing on three unique field datasets, BOMS provided more accurate wavelength predictions than each individual base model and other common ripple predictors.

Publication type Article
Publication Subtype Journal Article
Title A machine learning approach to predicting equilibrium ripple wavelength
Series title Environmental Modeling and Software
DOI 10.1016/j.envsoft.2022.105509
Volume 157
Year Published 2022
Language English
Publisher Elsevier
Contributing office(s) St. Petersburg Coastal and Marine Science Center
Description 105509, 13 p.
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