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Modified Hybrid Genetic Algorithm-Based Artificial Neural Network Application on Wall Shear Stress Prediction

Authors: Zohreh Sheikh Khozani, Wan Hanna Melini Wan Mohtar, Mojtaba Porhemmat

Abstract:

Prediction of wall shear stress in a rectangular channel, with non-homogeneous roughness distribution, was studied. Estimation of shear stress is an important subject in hydraulic engineering, since it affects the flow structure directly. In this study, the Genetic Algorithm Artificial (GAA) neural network is introduced as a hybrid methodology of the Artificial Neural Network (ANN) and modified Genetic Algorithm (GA) combination. This GAA method was employed to predict the wall shear stress. Various input combinations and transfer functions were considered to find the most appropriate GAA model. The results show that the proposed GAA method could predict the wall shear stress of open channels with high accuracy, by Root Mean Square Error (RMSE) of 0.064 in the test dataset. Thus, using GAA provides an accurate and practical simple-to-use equation.

Keywords: Artificial neural network, genetic algorithm, genetic programming, rectangular channel, shear stress.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.3462081

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References:


[1] N. Chien N, Z. H. Wan Mechanics of sediment transport, ASCE press, New York, USA (1999).
[2] P. Y. Julien Erosion and sedimentation, Cambridge University Press, Cambridge, U.K. (1995).
[3] S. R. Khodashenas, A. Paquier, River bed deformation calculated from boundary shear stress, J. Hydraul. Res. 40(5) (2002) 603-609.
[4] D. W. Knight, K. W. H. Yuen, A. A. I. Al Hamid, Boundary shear stress distributions in open channel flow, In: Physical Mechanisms of Mixing and Transport in the Environment, (K Beven, P Chatwin, J Millbank (eds)) Wiley New York, pp.51-87 (1994).
[5] T. P. Flintham, P. A. Carling, The prediction of mean bed and wall boundary shear in uniform and compositely rough channels, Proc. Int. Conf. River Regime, Wiley, Chichester, 1988.
[6] S. N. Ghosh, N. Roy, Boundary Shear Distribution in Open Channel Flow, J. Hydraul. Div. 96 (4) (1970) 967-994.
[7] I. Nezu, H. Nakagawa, Turbulence in open channel flows, IAHR Series, A. A. Balkema, Rotterdam, The Netherlands (1993).
[8] W. R. C. Myers, Momentum transfer in a compound channel, J. Hydraul. Res. 16 (2) (1978) 139-150.
[9] D. W. Knight, J. A. Macdonald, Open channel flow with varying bed roughness, J. Hydraul. Div. 105 (9) (1979) 1167-1183.
[10] D. W. Knight, J. D. Demetriou, M. E. Hamed, Boundary shear stress in smooth rectangular channel, J. Hydraul. Eng. 10 (4) (1984) 405-422.
[11] G. Seckin, N. Seckin, R. Yurtal, Boundary shear stress analysis in smooth rectangular channels, Can. J. Civ. Eng. 33 (2006) 336-342.
[12] D. W. Knight, Boundary shear in smooth and rough channels, J. Hydraul. Div. 107 (7) (1981) 839-851.
[13] Cokljat D, B.A. Younis, Second-order closure study of open-channel flows, J. Hydraul. Eng. 121(2) (1995) 94-107.
[14] Y. Zheng, Y. C. Jin, Boundary shear in rectangular ducts and channels, J. Hydraul. Eng. 124(1) (1998) 86-89.
[15] A. R. Zarrati, Y. C. Jin, S. Karimpour, Semianalytical model for shear stress distribution in simple and compound open channels, J. Hydraul. Eng. 134 (2) (2008) 205-215.
[16] K. Yang, R. Nie, X. Liu, S. Cao, Modeling Depth-Averaged Velocity and Boundary Shear Stress in Rectangular Compound Channels with Secondary Flows, J. Hydraul. Eng. 139 (1) (2013) 76-83.
[17] H. Bonakdari, Z. Sheikh, M. Tooshmalani, Comparison between Shannon and Tsallis entropies for prediction of shear stress distribution in circular open channels, Stoch. Environ. Res. Risk Assess. 29 (1) (2015) 1-11.
[18] M. I. Komurcu, N. Tutkun, I. H. Ozolcer, A. Akpinar, Estimation of the beach bar parameters using the genetic algorithms, Appl. Math. Comput. 195 (2008) 49-60.
[19] O. Kisi, J. Shiri, Sh. Shamshirband, Sh. Motamedi, D. Petkovic, R. Hashemi, A survey of water level fluctuation predicting in Urmia Lake using support vector machine with firefly algorithm, Appl. Math. Comput. 270 (2015) 731-743.
[20] Z. Sheikh Khozani, H. Bonakdari, A. H. Zaji, Application of soft computing technique in prediction percentage of shear force carried by walls in rectangular channel with Non-homogenous roughness, Water Sci. Technol. DOI: 10.2166/wst.2015.470.
[21] Z. Sheikh Khozani, H. Bonakdari, I. Ebtehaj, An analysis of shear stress distribution in circular channels with sediment deposition based on Gene Expression Programming, Int. J. Sediment Res. 32 (2017) 575-584.
[22] O. Kisi, A. Hosseinzadeh Dailr, M. Cimen, J. Shiri, Suspended sediment modeling using genetic programming and soft computing techniques, J. Hydrol. 450-451 (2012) 48-58.
[23] Z. Sheikh Khozani, H. Bonakdari, A. H. Zaji, Application of a genetic algorithm in predicting the percentage of shear force carried by walls in smooth rectangular channels, Measurement. 87 (2016) 87-98.
[24] A. H. Zaji, H. Bonakdari, Application of artificial neural network and genetic programming models for estimating the longitudinal velocity field in open channel junctions, Flow. Meas. Instrum. 41 (2015) 81-89.
[25] O. Kisi, The potential of different ANN techniques in evapotranspiration modelling, Hydrol. Process, 22 (2008) 2449-2460.
[26] O. Bilhan, M. Emin Emiroglu, O. Kisi, Application of two different neural network techniques to lateral outflow over rectangular side weirs located on a straight channel, Adv. Eng. Softw. 41(83) (2010) 1-7.
[27] O. Kisi, H. Kerem Cigizoglu, Comparison of different ANN techniques in river flow prediction, Civ. Eng. Environ. Syst. 24 (2007) 211-231.
[28] K. Levenberg, A method for the solution of certain non-linear problems in Least-Squares, Qu. Appl. Math. 2 (1944) 164-168.