Assessment of Shear Stress Distribution in Meandering Compound Channels with Differential Roughness Through Various Artificial Intelligence Approach

Accurate prediction of shear stress distribution along the boundary in an open channel is the key to solving numerous critical engineering problems such as flood control, sediment transport, riverbank protection, and others. Similarly, the estimation of flow discharge in flood conditions is also challenging for engineers and scientists. The flow structure in compound channels becomes complicated due to the transfer of momentum between the deep main channel and the adjoining floodplains, which affects the distribution of shear force and flow rate across the width. Percentage sharing of shear force at floodplain (%Sfp) is dependent on the non-dimensional parameters like width ratio of the channel $$(\alpha )$$ ( α ) , relative depth $$(\beta )$$ ( β ) , sinuosity $$(s)$$ ( s ) , longitudinal channel bed slope $$(S_{{\text{o}}} ),$$ ( S o ) , meander belt width ratio $$(\omega )$$ ( ω ) , and differential roughness $$(\gamma )$$ ( γ ) . In this paper, various artificial intelligence approaches such as multivariate adaptive regression spline (MARS), group method of data handling Neural Network (GMDH-NN), and gene-expression programming (GEP) are adopted to construct model equations for determining %Sfp for meandering compound channels with relative roughness. The influence of each parameter used in the model for predicting the %Sfp is also analyzed through sensitivity analysis. Statistical indices are employed to assess the performance of these models. Validation of the developed %Sfp model is performed for the experimental observations by conventional analytical models; to verify their effectiveness. Results indicate that the proposed GMDH-NN model predicted the %Sfp satisfactorily with the coefficient of determination (R2) of 0.98 and 0.97 and mean absolute percentage error (MAPE) of 0.05% and 0.04% for training and testing dataset, respectively as compared to GEP and MARS. The developed model is also validated with various sinuous channels having sinuosity 1.343, 1.91 and 2.06.