Determination of Discharge Distribution in Meandering Compound Channels Using Machine Learning Techniques

Abstract

Accurate flow rate prediction is essential to analyze flood control, sediment transport, riverbank protection, and so forth. The flow rate distribution becomes even more complicated in compound channels due to the momentum transfer between different subsections across the width of the channel. Conventional channel division methods estimate flow distribution at the main channel and floodplains by assuming a division line with zero apparent shear stress. The article attempts to develop a model to calculate the percentage of discharge in the main channel (%Qmc) using techniques such as Group Method of Data Handling—Neural Network (GMDH-NN) and gene-expression programming (GEP) by incorporating the effects of various geometric and hydraulic parameters. The paper proposes a modified channel division method with a variable-inclined interface, with zero apparent shear force distribution at the channel subsections according to the statistical indices employed to assess these models’ performance in predicting %Qmc. This variable-inclined interface changes its slope according to the channel parameters. The model’s effectiveness is verified by validating with experimental observations by conventional analytical methods.