Investigation of flow resistance in smooth open channels using artificial neural networks

Flow Measurement and Instrumentation

Volumn 19, Issue 6, 2008, Pages 404-408

  Bilgil, A ^a   Altun, H ^a  

^a NIGDE UNIVERSITY   (Turkey)

Author keywords

Artificial neural networks; Friction coefficient; Manning equation; Open channel

Indexed keywords

ARTIFICIAL INTELLIGENCE; BACKPROPAGATION; CIVIL ENGINEERING; ENERGY DISSIPATION; FLUID MECHANICS; FORECASTING; FRICTION; HYDRAULICS; KETONES; NETWORK PROTOCOLS; OPEN CHANNEL FLOW; SENSOR NETWORKS; SHORE PROTECTION; TRIBOLOGY; VEGETATION;

ARTIFICIAL NEURAL NETWORKS; FRICTION COEFFICIENT; MANNING EQUATION; OPEN CHANNEL;

NEURAL NETWORKS;

EID: 53349179562     PISSN: 09555986     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.flowmeasinst.2008.07.001     Document Type: Article

References (27)

1. Prandtl L. Essentials of fluid dynamics (1960), Blackie & Son Ltd, London and Glasgow print of
2. Keulegan G.H. Laws of turbulent flow in open channels. Jounal of National Bureau of Standards 1151 21 (1938) 707-741 Research paper
3. Chow V.T. Open channel hydraulics (1959), Mc. Graw-Hill Book Co, New York
4. Reinus E. Steady uniform flow in open channels transactions (1961), Royal Institute Technology, Stockholm (Sweden) p. 179
5. Tracy H.J., and Lester C.M. Resistance coefficients and velocity distribution-smooth rectangular channel. Water Supply Paper U.S. Geological Survey (1961) 1-18
6. Rao KK. Effect of shape on the mean-flow characteristics of turbulent flow through smooth rectangular open channel. Thesis presented to the University of Iowa at Iowa City, Iowa in Partial Fulfilment of the Requirements for the Degree of Doctor of Philosophy; 1969
7. Powell R.W. Resistance studies on smooth open channels. Journal of Hydraulic Division, ASCE (1970) 364-367
8. Pillia N.N. On uniform flow through smooth rectangular open channels. Journal of Hydraulics Research 8 (1970) 403-417
9. Pillia N.N. Effect of shape on uniform flow through smooth rectangular open channels. Journal of Hydraulics Research 8 (1997) 655-658
10. Kazemipour A.K., and Apelt C.J. Shape effects on resistance to uniform flow in open channels. Journal of Hydraulics Research 17 (1982) 129-147
11. Myers W.R.C. Flow resistance in wide rectangular channels. Journal of Hydraulic Division, ASCE 108 (1982) 471-482
12. Dooge J.C.I. The Manning formula in context channel flow resistance; centennial of Manning's formula (1991), Water Resources Publications, Littleton (Colo) p. 136-85
13. Syamala P. Velocity shear and friction studies in smooth rectangular channels of small aspect ratio for supercritical flows, Thesis presented to the Indian Institute of Science at Bangalore India in Partial Fulfilment of the Requirements for Degree of Doctor of Philosophy; 1988
14. Rahman H.K., Williams J.R., and Wormleaton P.R. Identification problem of Open channel friction parameters. Journal of Hydraulics Research 8 (1997) 1078-1088
15. Çi{dotless}ray C. On some special corner flows (with emphasis on uniform flows in rectangular channels), Memorial Volume of L. Prof. Dr. K. Çeçen, Faculty of Civil Engineering, ÏTÜ; 1999
16. Bilgil A. The effect of wall shear stress on friction factor in smooth open channel flows, Ph.D. Thesis. Trabzon (Turkey): Karadeniz Technical University; 1998 (in Turkish)
17. Yen B.C. Open channel flow resistance. Journal of Hydraulic Engineering 128 (2002) 20-39
18. Karunanithi N., Grenney W.J., Whitly D., and Bovee K. Noural networks for river flow prediction. Journal of Computing in Civil Engineering 8 (1994) 201-220
19. Flood I., and Kartam N. Neural network in civil engineering. I. Principles and understanding. Journal of Computing in Civil Engineering 8 2 (1994) 131-148
20. Grubert J.P. Application of neural networks in strafed flow stability analysis. Journal Hydraulic Engineering 12 (1995) 523-532
21. Minns A.W. Artifical neural networks as subsymbolic process descriptors (1998), Belkama, Rotterdam (The Netherlands)
22. Sanchez L., Arroyo V., Garcia J., Koev K., and Revilla J. Use of neural networks in design of coastal sewage system. Journal of Hydraulic Engineering 124 (1998) 457-464
23. Nagy H.M., Watanabe K., and Hirano M. Prediction of sediment load concentration in rivers using artificial neural network model. Journal of Hydraulic Engineering 128 (2002) 588-595
24. Cigizoglu H.K. Application of generalized regression neural networks to intermittent flow forecasting and estimation. Journal of Hydrologic Engineering (2005) 336-341
25. Cigizoglu H.K., and Alp M. Generalized regression neural network in modelling river sediment yield. Advances in Engineering Software 37 (2006) 63-68
26. Altun H., Bilgil A., and Fidan C.F. Treatment of multi-dimensional data to enhance neural network estimators in regression problems. Expert Systems with Applications 32 2 (2006). http://www.sciencedirect.com/ Available online from
27. Rumelhart D.E., Hinton G.E., and Williams R.J. Learning internal representation by error propagation. Parallel and distributed processing: Explorations in the microstructure of cognition. Vol 1: Foundations (1986), MIT Press, Cambridge, MA