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.

沙波纹是海底的地貌特征，影响包括波浪衰减和沉积物输送在内的底部边界层动力学。我们使用机器学习方法提出了一种新的平衡波纹预测器，该方法输出波浪产生的平衡波长的概率分布和统计数据，包括波纹高度的估计、最可能的波纹波长以及沉积物和流量参数化。贝叶斯最优模型系统 (BOMS) 是一个集成机器学习系统，它将两种机器学习算法和两种确定性经验波纹预测器与贝叶斯元学习器相结合，以在沙地位置产生概率波生成的平衡波纹波长估计。BOMS 的十倍交叉验证导致调整后的 R 平方值为 0。93 和 8.0 厘米的平均均方根误差 (RMSE)。在对三个独特的现场数据集进行交叉验证和测试期间，BOMS 提供了比每个单独的基本模型和其他常见波纹预测器更准确的波长预测。