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Sustainable Water Resources Management

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20-07-2019 | Original Article

Flow distribution in a compound channel using an artificial neural network

Authors: Jnana Ranjan Khuntia, Kamalini Devi, Kishanjit Kumar Khatua

Published in: Sustainable Water Resources Management | Issue 4/2019

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Abstract

In compound channels, much of the hydraulic resistance may be assigned by channel and floodplain geometry and bed roughness. Understanding the distribution of flow in the subsections of a compound river channel is a tedious work due to complex momentum transfer at the junction. The zonal discharges are mostly dependent upon the hydraulic resistance of the corresponding subsections. Hence, different experimentations in laboratory channels are required to be investigated by analyzing the dependence of momentum transfer on individual flow capacities of the subsections. So, experiments are performed in non-homogeneous roughness beds of asymmetric compound channels to examine the flow behavior. Total 272 numbers of experimental data sets comprising wide ranges of width ratio, relative flow depth, aspect ratio and roughness ratio with the present experimental data series are used for both training and validation of the model. Previous models can provide good results only for specific ranges of independent parameters whereas the back propagation of artificial neural network (BPNN) model is capable of performing well for the global ranges of independent parameters. This is because BPNN is able to perform the nonlinear mapping between the dependent and independent variables during the training. The efficacy of the models is verified with the standard statistical error analysis for the data sets. The BPNN model is found to perform well as compared to other researcher’s models.

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Title: Flow distribution in a compound channel using an artificial neural network
Authors: Jnana Ranjan Khuntia
Kamalini Devi
Kishanjit Kumar Khatua
Publication date: 20-07-2019
Publisher: Springer International Publishing
Published in: Sustainable Water Resources Management / Issue 4/2019
Print ISSN: 2363-5037
Electronic ISSN: 2363-5045
DOI: https://doi.org/10.1007/s40899-019-00341-2

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