PROBABILISTIC PREDICTIONS OF EQUILIBRIUM RIPPLE GEOMETRY FOR TIME-DEPENDENT SEAFLOOR MODELING
ICCE 2022
PDF

How to Cite

PROBABILISTIC PREDICTIONS OF EQUILIBRIUM RIPPLE GEOMETRY FOR TIME-DEPENDENT SEAFLOOR MODELING. (2023). Coastal Engineering Proceedings, 37, sediment.82. https://doi.org/10.9753/icce.v37.sediment.82

Abstract

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.
PDF

References

Chen, Guestrin (2016): XGBoost: A Scalable Tree Boosting System, pp. 785–794.

Faraci, Foti (2002): Geometry, migration and evolution of small-scale bedforms generated by regular and irregular waves. Coastal Engineering, 47, pp. 35–52.

Friedman (2002): Stochastic Gradient Boosting. Computational Statistics & Data Analysis, 38, pp. 367–378.

Mogridge, Davies, Willis (1994): Geometry prediction for wave-generated bedforms, Coastal Engineering 22, pp. 255–286.

Nelson, Voulgaris, Traykovski (2013): Predicting waveinduced ripple equilibrium geometry, Journal of Geophysical Research: Oceans, 118, pp. 3202–3220.

Nelson, Voulgaris (2015): A spectral model for estimating temporal and spatial evolution of rippled seabeds, Ocean Dynamics, 65, pp. 155–171.

Nielsen (1981): Dynamics and geometry of wavegenerated ripples. Journal of Geophysical Research: Oceans, 86, pp. 6467–6472.

Penko, Calantoni, Hefner (2017): Modeling and observations of sand ripple formation and evolution during TREX13, IEEE Journal of Oceanic Engineering, 42, pp. 260–267.

Phillip, Penko, Palmsten, DuVal (2022): A machine learning approach to predicting equilibrium ripple wavelength, Environmental Modelling & Software, https://doi.org/10.1016/j.envsoft.2022.105509.

Soulsby, Whitehouse (2005): Prediction of ripple properties in shelf seas-Mark 2 predictor for time evolution-Final technical report.

Soulsby, Whitehouse, Marten (2012): Prediction of timeevolving sand ripples in shelf seas, Continental Shelf Research, 38, pp. 47–62.

Traykovski, Hay, Irish, Lynch (1999): Geometry, migration, and evolution of wave orbital ripples at LEO-15, Journal of Geophysical Research: Oceans, 104, pp. 1505–1524.

Traykovski (2007): Observations of wave orbital scale ripples and a nonequilibrium time-dependent model. Journal of Geophysical Research: Oceans, 112.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright (c) 2023 Allison M. Penko, Ryan E. Phillip