Genetic algorithm optimized neural network prediction of the friction factor in a mobile bed channel

Journal of Intelligent Systems

Volumn 19, Issue 4, 2010, Pages 315-335

  Kumar, Bimlesh ^a   Bhatla, Ankit ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY GUWAHATI   (India)

Author keywords

Bed particles; Flow depth; Flow velocity; Friction factor

Indexed keywords

BACKPROPAGATION; BACKPROPAGATION ALGORITHMS; DATA MINING; FLOW VELOCITY; FORECASTING; FRICTION; GENETIC ALGORITHMS; NEURAL NETWORKS; TRIBOLOGY;

BACK PROPAGATION NEURAL NETWORKS; BED PARTICLES; FLOW DEPTH; FRICTION FACTORS; HYDRAULIC ENGINEERS; MUTUAL INTERACTION; NEURAL NETWORK PREDICTIONS; NON-LINEAR RELATIONSHIPS;

COMPLEX NETWORKS;

EID: 79551717505     PISSN: 03341860     EISSN: None     Source Type: Journal    
DOI: 10.1515/JISYS.2010.19.4.315     Document Type: Article

References (35)

1. Baker, J.E. 1987. Reducing Bias and Inefficiency in the Selection Algorithm, Proceedings of the Second International Conference on Genetic Algorithms and their Application, Hillsdale, New Jersey, USA: Lawrence Erlbaum Associates.
2. nd Edition, University of Alberta Press, Canada.
3. Brownlie, W.R. 1981. Compilation of alluvial channel data: Laboratory and field, Rep. No. KH-R-43B, California Institute of Technology, California.
4. Chien, N. 1956. The Present Status of Research on Sediment Transport, Trans. ASCE, 121, 833-868.
5. Einstein, H.A., Barbarossa, N.L. 1952. River Channel roughness, Trans. ASCE, 117, 1121-1146.
6. Engelund, F. (1966), Hydraulic resistance of alluvial streams, Journal of Hydraulic Division, ASCE, 92(2), 315-326
7. Engelund, F. and Fredsoe, J. 1982. A sediment transport model for straight alluvial channels, Nordic Hydrology, 7, 293-306.
8. Fadare, D.A. and Ofidhem U.I. 2009. Artificial neural network model for prediction of friction factor in Pipe flow, Journal of Applied Sciences Research, 5(6), 662-670.
9. Garde, R.J. and Ranga Raju, K.G. 1966. Resistance relationship for alluvial channel flow, Journal of Hydraulic Division, ASCE, 92, HY4, 77-100.
10. Garson G.D. 1991. Interpreting neural-network connection weights, Artificial Intelligence Expert, 6, 47-51.
11. Gevrey M., Dimopoulos I. and Lek S. 2003. Review and comparison of methods to study the contribution of variables in artificial neural network models, Ecological Modelling, 160, 249-264.
12. Gilbert, G.K. 1914. The Transport of Debris by Running Water, Professional Paper 86, U.S. Geological Survey.
13. Haynie, R.M. and Simons, D.B. 1968. Design of Stable Channels in alluvial materials, Journal of Hydraulics Division, ASCE, 94, HY6, 1399-1420.
14. Kennedy, J. F. 1961. Further Laboratory Studies of the Roughness and Suspended Load of alluvial Streams, Rept. No. KH-R-3, W. M. Keck Lab. of Hydraulics and Water Resources, Cal Tech. USA.
15. Kumar, B. and Rao, ARK. 2010. Metamodeling approach to predict friction factor in alluvial channel, Computers and Electronics in Agriculture, 70, 144-150.
16. Lacey.G. 1946. A General Theory of flow in Alluvium, Proceedings of the Institute of Civil Engineers, London, 27, 1.
17. Lek, S., Delacoste, M., Baran, P., Dimopoulos, I., Lauga, J. and Aulagnier S. 1996. Application of neural networks to modeling nonlinear relationships in ecology, Ecological Modelling, 90,39-52.
18. Leopold, L.B. and Maddock, T. Jr. 1953. The Hydraulic Geometry of Stream Channels and Some Physiographic Implications, U.S. Geol. Survey, Prof. Paper 252.
19. Langbein, W. 1942. Hydraulic criteria for sand waves, Transactions AGU, 23, 2, 615-618.
20. Nordin, C.F. Jr. 1971. Statistical properties of dune profiles, USGS Professional Papers, 562-F.
21. Olden, J.D. and Jackson, D.A. 2002. Illuminating the "blackbox" : under-standing variable contributions in artificial neural networks, Ecological Modelling, 154, 135-150.
22. Ozesmi S.L. and Ozesmi U. 1999. An artificial neural network approach to spatial habitat modeling with inters specific interaction, Ecological Modelling, 116, 15-31.
23. Raudkivi, A. J. 1967. Analysis of Resistance in Fluvial Channels, ASCE Journal Hydraulics Division, 93(5), 73-84.
24. Rumelhart, D.E., Hinton, G. E., and Williams, R.J. 1986. Learning internal representations by error propagation. In Paralled Distributed Processing. Explorations in the Microstructure of Cognition. Volume I: Foundations, edited by Rumelhart, D.E., McClelland, J.L. and the PDP Research Group, Cambridge, MA, The MIT Press, 318-362.
25. Scardi M. and Harding L. W. 1999, Developing an empirical model of phytoplankton primary production: a neural network case study, Ecological Modelling, 120, 213-223.
26. Schleiter I.M., Borchardt D., Wagner R., Dapper T., Schmidt K.D., Schmidt, H.H. and Werner H. 1999. Modeling water quality, bioindication and population dynamics in lotie ecosystems using neural networks, Ecological Modelling, 120, 271-286.
27. Shen, H. W. 1962. Development of Bed Roughness in Alluvial Channels, ASCE Journal Hydraulics Division, ASCE, 88(3), 45-58.
28. Simons, D. B. and Richardson, E.V. 1963. Forms of Bed Roughness in Alluvial Channels, Transactions ASCE, 128(1), 284-302.
29. Simons, D. B. and Richardson, E.V. 1966. Resistance to flow in alluvial channels, U.S. Geological Survey Professional Paper 422-J.
30. Smith, K.V.H. 1968. Alluvial channel resistance related to bed form. ASCE Journal Hydraulics Division, 94(1), 59-69.
31. Vanoni, V.A. and Brooks, N.H. 1957. Laboratory Studies of the Roughness and Suspended Load of Alluvial Streams, M.R.D. Sediment Series No. 11, California Institute of Technology Sedimentation Laboratory, California.
32. Walid, H.S. and Shyam, S.S. 1998. An artificial neural network for non-iterative calculation of the friction factor in pipeline flow, Computers and Electronics in Agriculture, 21, 219-28.
33. White, W.R., Paris, E. and Bettess, R. 1981. The frictional characteristics of alluvial streams: a new approach, Proceedings of the Institution of Civil Engineers, London, Part 2, 69, 737-750.
34. Yen BC. 2002. Open Channel Flow Resistance, Journal of Hydraulic Engineering, 128(1), 20-39.
35. Yuhong, Z. and Wenxin, H. 2008. Application of artificial neural network to predict the friction factor of open channel flow, Communications in Nonlinear Science and Numerical Simulation. 14,2373-2378.