Mean bed shear stress estimation in a rough rectangular channel using a hybrid genetic algorithm based on an artificial neural network and genetic programming

Document Type : Article


Department of Civil Engineering, Razi University, Kermanshah, Iran


The determination of erosion and deposition patterns in channels requires detailed knowledge and estimations of the bed shear stress. In this investigation, the application of a Genetic Algorithm based Artificial (GAA) neural networkand genetic programming (GP) for predicting bed shear stress in a rectangular channel with rough boundaries. Several input combinations, fitness functions and transfer functions were investigated to determine the best GAA model. Also the effect of various GP operators on estimating bed shear stress was studied. The comparison between the GAA and GP technique abilities in predicting bed shear stress were investigated. The results revealed that the GAA model performs better in predicting the bed shear stress (RMSE = 0.0774), as compared to the GP model (RMSE = 0.0835).


Main Subjects

1. Knight, D.W., Demetriou, J.D. and Hamed, M.E.
Boundary shear in smooth rectangular channels", J.
Hydraul. Eng., 10, pp. 405-422 (1984).
2. Knight, D.W. Boundary shear in smooth and rough
channels", J. Hydraul. Div., 107, pp. 839-851 (1981).
3. Tominaga, A., Nezu, I., Ezaki, K. and Nakagawa,
H. Three-dimensional turbulent structure in straight
open channel
ows", J. Hydraul. Res., 27, pp. 149-173
4. Bonakdari, H., Tooshmalani, M. and Sheikh, Z.
Predicting shear stress distribution in rectangular
channels using entropy concept", Int. J. Eng., 28(3),
pp. 357-364 (2015).
5. Alhamid, A.A.I. Boundary shear stress and velocity
distributions in di erentially roughened trapezoidal
open channels", PhD Thesis, The University of Birmingham
6. Sheikh, Z. and Bonakdari, H. Prediction of boundary
shear stress in circular and trapezoidal channels with
Entropy concept", Urban Water J., 13(6), pp. 629-636
7. Knight, D.W., Yuen, K.W.H. and Al Hamid, A.A.I.
Boundary shear stress distributions in open channel

ow", In: K. Beven, P. Chatwin, J. Millbank (Eds.),
Physical Mechanisms of Mixing and Transport in the
Environment, Wiley New York, pp. 51-87 (1994).
8. Ardiclioglu, M., Sekcin, G. and Yurtal, R. Shear stress
distributions along the cross section in smooth and
rough open channel
ows", Kuwait J. Sci. Eng., 33,
pp. 155-68 (2006).
9. Guo, J. and Julien, P.Y. Shear stress in smooth rectangular
ow", J. Hydraul. Eng., 131(1),
pp. 30-37 (2005).
10. Babaeyan-Koopaei, K., Ervine, D.A., Carling, P.A.
and Cao, Z. Velocity and turbulence measurements
for two overbank
ow events in River Severn", J.
Hydraul. Eng., 128, pp. 891-900 (2002).
11. Julien, P.Y., Erosion and Sedimentation, U.K. Cambridge
University Press (1995).
12. Berlamont, J.E., Trouw, K. and Luyckx, G. Shear
stress distribution in partially lled pipes", J. Hydraul.
Eng., 129(9), pp. 697-705 (2003).
13. Han, Y., Yang, S.Q. and Dharmasiri, N. Application
of main
ow data in the determination of boundary
shear stress in smooth closed ducts", World Environmental
and Water Resources Congress: Crossing
Boundaries, Albuquerque, NM (2012).
14. Yang, S.Q., Dharmasiri, N. and Han, Y. Momentum
balance method and estimation of boundary shear
stress distribution", J. Hydraul. Eng., 138, pp. 657-
660 (2012).
15. Knight, D.W. and Sterling, M. Boundary shear in
circular pipes partially full", J. Hydraul. Eng., 126(4),
pp. 263-275 (2000).
16. Bonakdari, H., Sheikh, Z. and Tooshmalani, M. Comparison
between Shannon and Tsallis entropies for
prediction of shear stress distribution in circular open
channels", Stoch. Env. Res. Risk Assess., 29(1), pp.
1-11 (2015).
17. Sheikh Khozani, Z., Bonakdari, H. and Ebtehaj, I. An
analysis of shear stress distribution in circular channels
with sediment deposition based on gene expression
programming", Int. J. Sediment Res. (2017). DOI:
org/10.1016/j.ijsrc.2017.04.004 (2017).
18. Gharagheizi, F., Ilani-Kashkouli, P., Farahani, N. and
Mohammadi, A.H. Gene expression programming
strategy for estimation of
ash point temperature
of non-electrolyte organic compounds", Fluid Phase.
Equilibria., 329, pp. 71-77 (2012).
19. Sheikh Khozani, Z., Bonakdari, H. and Zaji, A.H.
Estimating the shear stress distribution in circular
channels based on the randomized neural networks
technique", Appl. Soft Comp., 58, pp. 144-148 (2017).
20. Najafzadeh, M., Barani, G.A. and Hessami Kermani,
M.R. GMDH based back propagation algorithm to
predict abutment scour in cohesive soils", Ocian Eng.,
59, pp. 100-106 (2013).
21. Kisi, O., Emin Emiroglu, M.E., Bilhan, O. and Guven,
A. Prediction of lateral out
ow over triangular
labyrinth side weirs under subcritical conditions using
soft computing approaches", Expert Syst. Appl., 39,
pp. 3454-3460 (2012).
22. Shiri, J., Ashraf Sadraddini, A., Nazemi, A.H., Kisi,
O., Landeras, G., Fakheri Fard, A. and Marti, P.
Generalizability of gene expression programmingbased
approaches for estimating daily reference evapotranspiration
in coastal stations of Iran", J. Hydrol.,
508, pp. 1-11 (2014).
23. Sheikh Khozani, Z., Bonakdari, H. and Zaji, A.H.
Using two soft computing methods in prediction wall
and bed shear stress in smooth rectangular channels",
Appl. Water Sci. (2017). DOI: 10.1007/s13201-017-
0548-y (2017).
24. Azamathulla, H.M. and Zahiri, A. Flow discharge
prediction in compound channels using linear genetic
programming", J. Hydrol., 454-455, pp. 203-207
25. Zaji, A.H. and Bonakdari, H. Performance evaluation
of two di erent neural network and particle swarm
optimization methods for prediction of discharge capacity
of modi ed triangular side weirs", Flow Meas.
Instrum., 40, pp. 149-156 (2014).
26. Tayfur, G. Arti cial neural networks for sheet sediment
transport", Hydrol. Sci. J., 47, pp. 879-892
27. Kisi, O., Dailr, A.H., Cimen, M. and Shiri, J. Suspended
sediment modeling using genetic programming
and soft computing techniques", J. Hydrol., 450-451,
pp. 48-58 (2012).
Z. Sheikh Khozani et al./Scientia Iranica, Transactions A: Civil Engineering 25 (2018) 152{161 161
28. Cobaner, M., Seckin, G., Seckin, N. and Yurtal, R.
Boundary shear stress analysis in smooth rectangular
channels and ducts using neural networks", Water
Environ. J., 24, pp. 133-139 (2010).
29. Sheikh Khozani, Z., Bonakdari, H. and Zaji, A.M.
Application of soft computing technique in prediction
percentage of shear force carried by walls in rectangular
channel with non-homogenous roughness", Water
Sci. Technol., 73(1), pp. 124-129 (2016).
30. Sheikh Khozani, Z., Bonakdari, H. and Zaji, A.M.
Application of a genetic algorithm in predicting the
percentage of shear force carried by walls in smooth
rectangular channels", Measurement, 87, pp. 87-98
31. Zadeh, M.R., Amin, S., Khalili, D. and Singh, V.P.
Daily out
ow prediction by multi layer perceptron
with logistic sigmoid and tangent sigmoid activation
functions", Water Resour. Manage., 24, pp. 2673-2688
32. Emiroglu, M.E., Bilhan, O. and Kisi, O. Neural networks
for estimation of discharge capacity of triangular
labyrinth side-weir located on a straight channel",
Expert Sys. Appl., 38, pp. 867-874 (2011).
33. Pierini, J.O., Gomez, E.A. and Telesca, L. Prediction
of water
ows in Colorado River, Argentina. Lat Am",
J. Aquatic. Res., 40, pp. 872-880 (2012).
34. Levenberg, K. A method for the solution of certain
non-linear problems in Least-Squares", Qu. Appl.
Math., 2(2), pp. 164-168 (1944).
35. Koza, J.R. Genetic programming as a means for
programming computers by natural selection", Stat.
Comput., 4, pp. 87-112 (1994).