Insights into the prediction capability of roughness coefficient in current ripple bedforms under varied hydraulic conditions

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Abstract Ubiquitous flow bedforms such as ripples in rivers and coastal environments can affect transport conditions as they constitute the bed roughness elements. The roughness coefficient needs to be adequately quantified owing to its significant influence on the performance of hydraulic structures and river management. This work intended to evaluate the sensitivity and robustness of three machine learning (ML) methods, namely, Gaussian process regression (GPR), artificial neural network (ANN), and support vector machine (SVM) for the prediction of the Manning's roughness coefficient of channels with ripple bedforms. To this end, 840 experimental data points considering various hydraulic conditions were prepared. According to the obtained results, GPR was found to accurately predict the Manning's coefficient with input parameters of Reynolds number (Re), depth to width ratio (y/b), the ratio of the hydraulic radius to the median grain diameter (R/D50), and grain Froude number (). Moreover, sensitivity analysis was implemented with proposed ML approaches which indicated that the ratio of the hydraulic radius to the median grain diameter has a considerable role in modeling the Manning's coefficient in channels with ripple bedforms.