Flow Discharge Determination in Straight Compound Channels Using ANNs

Although many researchers have studied the flow hydraulics in compound channels, there are still many complicated problems in determination of their flow rating curves. Many different methods have been presented for these channels but extending them for all types of compound channels with different geometrical and hydraulic conditions is certainly difficult. In this study, by aid of nearly 400 laboratory and field data sets of geometry and flow rating curves from 30 different straight compound sections and using artificial neural networks (ANNs), flow discharge in compound channels was estimated. 13 dimensionless input variables including relative depth, relative roughness, relative width, aspect ratio, bed slope, main channel side slopes, flood plains side slopes and berm inclination and one output variable (flow discharge), have been used in ANNs. Comparison of ANNs model and traditional method (divided channel method-DCM) shows high accuracy of ANNs model results. The results of Sensitivity analysis showed that the relative depth with 47.6 percent contribution, is the most effective input parameter for flow discharge prediction. Relative width and relative roughness have 19.3 and 12.2 percent of importance, respectively. On the other hand, shape parameter, main channel and flood plains side slopes with 2.1, 3.8 and 3.8 percent of contribution, have the least importance.

Authors:

• A. Zahiri
• A. A. Dehghani

Keywords:

• ANN model
• compound channels
• divided channel method (DCM)
• flow rating curve

References:

[1] L. A. Martin, and W. R. C. Myers, "Measurement of overbank flow in a
compound river channel," Proc. of Instn. Civil. Engineering, England,
Part 2, pp. 645-657, 1991.
[2] W. Liu, and C. S. James, "Estimating of discharge capacity in
meandering compound channels using artificial neural networks,"
Canadian Journal of Civil Engineering, Vol.27, No.2, pp. 297-308,2000.
[3] V. T. Chow, Open channel hydraulics, McGraw-Hill, London, 1959.
[4] P. R. Wormleaton, and D. J. Merrett, "An improved method of
calculation for steady uniform flow in prismatic main channel/flood
plain sections," Journal of Hydraulic Research, IAHR, Vol.28, pp. 157-174, 1990.
[5] K. Shiono, and D. W. Knight, "Mathematical models of flow in two or
multi stage straight channels," Int. Conf. on River Flood Hydraulics,
England, pp. 229-238, 1990.
[6] J. B. Wark, P. G. Samuels, and D. A. Ervine, "A practical method of
estimating velocity and discharge in compound channels," Int. Conf.
River Flood Hydraulics, England, pp. 163-172, 1990.
[7] P. Ackers, "Stage-Discharge functions for two-stage channels," Water
and Environmental Management, Vol. 7, pp. 52-61, 1993
[8] W. R. C. Myers, and J. F. Lyness, "Discharge ratios in smooth and
rough compound channels," Journal of Hydraulics Engineering, ASCE,
Vol.123, No.3, pp. 182-188, 1997.
[9] M. F. Lambert, and W. R. C. Myers, "Estimating the discharge capacity
in straight compound channels," Engrs Wat., Marit. and Energy,
Vol.130, pp. 84-94, 1998.
[10] D. Bousmar, and Y. Zech, "Momentum transfer for practical flow
computation in compound channels," Journal of Hydraulic Engineering,
ASCE, Vol 125, No. 7, pp. 696-706, 1999.
[11] D. A. Ervine, K. Babaeyan-Koopaei, and R. H. J. Sellin, "Two-
Dimensional solution for straight and meandering overbank flows,",
Journal of Hydraulic Engineering, ASCE, Vol.126, No.9, pp. 653-669,2000.
[12] S. Atabay, "Stage-discharge, resistance and sediment transport
relationships for flow in straight compound channels," Ph.D. dissertation, Dept. Civ. Eng., The University of Birmingham, UK, 2001.
[13] D. W. Knight, K. Shiono, and J. Pirt, "Prediction of depth mean
velocity and discharge in natural rivers with overbank flow," Int. Conf.
on Hydraulics and Environmental Modeling of Coastal, Estuarine and
River Waters, England, pp. 419-428, 1989.
[14] B. Abril, and D. W. Knight, "Stage-Discharge prediction for rivers in
flood applying a depth-averaged model," Journal of Hydraulic
Research, IAHR, Vol. 122, No. 6, pp 616-629, 2004.
[15] D. E. Rumelhart, G. E. Hinton, and R. J. Williams, 1986. "Learning
internal representation by error propagation," Parallel distributed
processing. Volume 1, D. E. Rumelhart and J. L. McClellend, EDs, MIT
Press, Cambridge, 1986.
[16] H. M. Nagy, K. Watanabe, and M. Hirano, "Prediction of sediment load
concentration in rivers using artificial neural network model," Journal of
Hydraulic Engineering, ASCE, Vol. 128, No.6, pp. 588-595, 2002.
[17] O. Kisi, "River flow modeling using artificial neural networks," Journal
of Hydrologic Engineering, ASCE, Vol.9, No.1, pp. 60-63, 2004.
[18] B. C. W. Dowson, and R. Wilby, "An artificial neural networks
approach to rainfall-runoff modeling," Journal of Hydrologic Science,
Vol.43, No.1, pp. 47-66, 1998.
[19] J. P. Suen, and W. Eheart, "Evaluation of neural networks for modeling
nitrate concentration in rivers," Journal of Water Resource Planning and
Management, ASCE, Vol.129, No.6, pp. 505-510, 2003.
[20] R. S. Govindaraju, and A. R. Rao, "Artificial neural networks in
hydrology," Kluwer Academic, Dordrecht, The Netherlands, 2000.
[21] A. M. Negm, A. M. Al-Brahim, and A. A. Al-Hamid, 2002. "Combinedfree
flow over weirs and below gates," Journal of Hydraulic Research,
IAHR, Vol.40, No.3, pp. 359-365, 2002.
[22] B. Bhattacharya, and D. P. Solomatine, "Applied of artificial neural
network in stage-discharge relationships," 4th Int. Conf. on
Hydroinformatics, USA, 2000.
[23] A. Bilgil, and H. Altun, "Investigation of flow resistance in smooth
open channels using artificial neural networks,"
Flow Measurement and Instrumentation, Vol. 19, No. 6, pp. 404-408,
2008.
[24] G. Seckin, S. Mevlut, M. Cobaner and T. Haktanir, "Application of
ANN techniques for estimating backwater through bridge constrictions
in Mississippi River basin," Advances in Engineering Software, Vol.40,
pp. 1039-1046, 2009.