A machine learning approach to predicting equilibrium ripple wavelength. (November 2022)

• Record Type: Journal Article
• Authors: Phillip, R.E.; Penko, A.M.; Palmsten, M.L.; DuVal, C.B.
• Is Part Of: Environmental modelling & software. Volume 157(2022)
• Publication Date: 2022-11
• Subjects: Sand ripples -- Bedforms -- Seafloor -- Machine learning -- Probabilistic prediction; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2022.105509
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: 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. Highlights: Developed and validated a probabilistic equilibrium ripple predictor. Utilized a stacked generalization ensemble machine learning technique. Predictions of ripple wavelength resulted in an error of 8.0 cm and an R 2 of 0.93.