Flow resistance co‐efficient of meandering river in alluvial plain and its prediction using artificial neural network

Abstract

AbstractA 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.