Estimation of Manning Roughness Coefficient in Alluvial Rivers with Bed Forms Using Soft Computing Models

Flow conditions (flow discharge, flow depth, and velocity) in natural streams are mainly determined via the resistance formula such as Manning’s equation. Evaluating accurate roughness coefficient (n), especially rivers with bed form during floods, to obtain more reliable results has always been of interest scholars. The interaction between is very complex since control forms, vice versa. main goal present study predict n using soft computing models, including multilayer perceptron artificial neural network (MLPNN), group method data handling (GMDH), support vector machine (SVM) model, genetic programming model (GP). To this end, energy grade line ( $${S}_{f}$$ ), Froude number (Fr), relative submergence $$y/{d}_{50}$$ ; y = depth d50 sediment size), dimensionless parameters $$\Delta /{d}_{50}$$ , /\lambda$$ /y$$ ∆ height λ length) were used input variables, was output variable. showed that all test models have acceptable accuracy, while SVM highest level accuracy determination $${R}^{2}=0.99$$ verification stage. sensitivity analysis MLPNN structural GMDH GP indicated most important affecting Fr, .