A proper estimation of flow resistance coefficient of river is essential for precise simulations of river hydraulics. In addition to the cross-sectional geometry and hydraulic parameters, the alignment of the channel affects the flow resistance coefficient in case of meandering rivers. In the present study, a rigorous field study of 131 km along the Barak River was conducted to assess the influence of meandering on the flow resistance coefficient. The values of flow resistance co-efficient were calculated using Chezy and Manning's equations with measured field data and the values from both are compared. However, the variation in the flow resistance co-efficient along the channel calculated from Manning's equation is significantly less as it does not consider the undulation and meandering. Using these field data, an artificial neural network (ANN) model has been developed to predict the cross-sectional averaged flow resistance for meandering river. The model considered the influence of relative curvature, depth of flow, bed particle size, Froude number and Reynolds number including water temperature for accurate predictions of flow resistance coefficient. The ANN model was tested and validated using 237 field data sample. The values of the statistical parameters indicate a very good fit to the training dataset with coefficient of determination (R²) = 0.9566 for training and good fit for testing with R² = 0.8131. The developed ANN model has been compared with other model with the same data set to check its applicability.

中文翻译：

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冲积平原曲流河流阻系数及其人工神经网络预测

正确估计河流的流阻系数对于河流水力学的精确模拟至关重要。除了横截面几何形状和水力参数外，河道的排列也会影响蜿蜒河流的流阻系数。在本研究中，对巴拉克河沿线 131 公里进行了严格的实地研究，以评估曲流对流阻系数的影响。使用 Chezy 和 Manning 方程结合现场实测数据计算流阻系数值，并对两者的值进行比较。然而，曼宁方程计算的沿河道流阻系数的变化要小得多，因为它没有考虑起伏和曲流。利用这些现场数据，开发了人工神经网络（ANN）模型来预测曲流河的横截面平均流阻。该模型考虑了相对曲率、水流深度、床层粒径、弗劳德数和雷诺数（包括水温）的影响，以准确预测流阻系数。使用 237 个现场数据样本对 ANN 模型进行了测试和验证。统计参数的值表明非常适合训练数据集，训练的确定系数 ( R ^{2 ) = 0.9566，并且}R ² = 0.8131的测试非常适合 。将开发的 ANN 模型与具有相同数据集的其他模型进行比较，以检查其适用性。