Sustainable Water Resources Management

, Volume 5, Issue 4, pp 1847–1858 | Cite as

Flow distribution in a compound channel using an artificial neural network

  • Jnana Ranjan KhuntiaEmail author
  • Kamalini Devi
  • Kishanjit Kumar Khatua
Original Article


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.


Compound channel Artificial neural network Flow distribution Roughness Relative flow depth Width ratio 



Flood channel facility


Artificial neural network


Back propagation neural network


Mean square error


Mean absolute percentage error


Mean absolute error


Mean percentage error


Root mean square error

List of symbols

\(\% Q_{\text{mc}}\)

Percentage of flow carried by the main channel


Velocity in the main channel

\(\% S_{\text{fp}}\)

Percentage shear force carried by flood plain

\(\% S_{\text{mc}}\)

Percentage shear force carried by the main channel


Perimeter in flood plain


Perimeter in the main channel


Velocity in flood plain


Bottom width of the main channel


Friction factor in the main channel


Friction factor in the main channel

\(\delta^{ *}\)

Flow aspect ratio


Total width of compound channel


Acceleration due to gravity


Bank full depth


Depth of flow over the main channel


Side slope of the main channel


Wetted perimeter


Longitudinal bed slope


Average velocity


Width ratio


Relative flow depth


Roughness ratio


Kinematic viscosity


Main channel aspect ratio



First author wishes to acknowledge the National Institute of Technology, Rourkela for financial assistance in the form of institute fellowship. The authors wish to thank the previous researchers for their quality experimental data sets. The authors gratefully acknowledge the comments of the anonymous reviewers and the editor, which enormously improved the presentation of the manuscript.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Civil Engineering DepartmentNational Institute of TechnologyRourkelaIndia
  2. 2.Vidya Jyothi Institute of Technology (VJIT)HyderabadIndia

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