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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
  • 21 Downloads

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.

Keywords

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

Abbreviations

FCF

Flood channel facility

ANN

Artificial neural network

BPNN

Back propagation neural network

MSE

Mean square error

MAPE

Mean absolute percentage error

MAE

Mean absolute error

MPE

Mean percentage error

RMSE

Root mean square error

List of symbols

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

Percentage of flow carried by the main channel

\(U_{\text{mc}}\)

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

\(P_{\text{fp}}\)

Perimeter in flood plain

\(P_{\text{mc}}\)

Perimeter in the main channel

\(U_{\text{fp}}\)

Velocity in flood plain

\(b_{2}\)

Bottom width of the main channel

\(f_{\text{fp}}\)

Friction factor in the main channel

\(f_{\text{mc}}\)

Friction factor in the main channel

\(\delta^{ *}\)

Flow aspect ratio

B

Total width of compound channel

g

Acceleration due to gravity

h

Bank full depth

H

Depth of flow over the main channel

n

Side slope of the main channel

R

Wetted perimeter

S0

Longitudinal bed slope

U

Average velocity

α

Width ratio

β

Relative flow depth

γ

Roughness ratio

ν

Kinematic viscosity

\(\delta\)

Main channel aspect ratio

Notes

Acknowledgements

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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