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.

无处不在的流动床型，例如河流和沿海环境中的涟漪，会影响运输条件，因为它们构成了床的粗糙度要素。由于粗糙度系数对水工结构和河流管理的性能有重大影响，因此需要对其进行充分量化。这项工作旨在评估三种机器学习 (ML) 方法的灵敏度和鲁棒性，即高斯过程回归 (GPR)、人工神经网络 (ANN) 和支持向量机 (SVM)，用于预测曼宁粗糙度系数。具有波纹床型的通道。为此，准备了 840 个考虑各种水力条件的实验数据点。根据得到的结果，发现探地雷达能够准确预测曼宁'Re )、深宽比 ( y/b )、水力半径与中值晶粒直径的比值 ( R/D ₅₀ ) 和晶粒弗劳德数 ( ⁠ ⁠ )。此外，使用建议的 ML 方法实施了敏感性分析，该方法表明水力半径与中值粒径的比率在模拟具有波纹床身的通道中的曼宁系数方面具有重要作用。