Prediction of roughness coefficient of a meandering open channel flow using Neuro-Fuzzy Inference System

Measurement: Journal of the International Measurement Confederation

Volumn 51, Issue 1, 2014, Pages 112-123

  Moharana, S ^a   Khatua, K K ^a  

^a NATIONAL INSTITUTE OF TECHNOLOGY   (India)

Author keywords

Aspect ratio; Fuzzy inference system; Meandering channel; Open channel; Roughness ratio; Sinuosity

Indexed keywords

ASPECT RATIO; FUZZY SYSTEMS; OPEN CHANNEL FLOW; WATER RESOURCES;

FUZZY INFERENCE SYSTEMS; MEANDERING CHANNEL; OPEN CHANNELS; ROUGHNESS RATIO; SINUOSITY;

FORECASTING;

EID: 84894533132     PISSN: 02632241     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.measurement.2014.01.026     Document Type: Article

References (37)

1. V.T. Chow Open-channel hydraulics 1959 McGraw-Hill Book Co. New York
2. G.J. Acrement, V.R. Schneider Jr. Guide for selecting Manning's roughness coefficients for natural channels and flood plains. US Geological Survey Water-Supply paper 2339, Federal Center, Colo., 1989.
3. K.K. Khatua, K.C. Patra, P. Nayak. Meandering effect for evaluation of roughness coefficients in open channel flow, in: Sixth Int Conf on River Basin Management, WIT Transactions on Ecology and the Environment, CMEM, WIT Press, 146(6) (2011), pp. 213-227 (ISSN 1743-3541).
4. W.L. Cowan Estimating hydraulic roughness coefficients Agric. Energy 37 1956 473 475
5. Hydraulic capacity of meandering channels in straight floodways. in: Tech. Memorandum, US Army Corps of Engineers, Waterways Experiment Station, Vicksburg, Miss, 1956, pp. 2-429.
6. Guide for selecting roughness coefficient n values for channels. in: Soil Cons. Service, US Dept. of Agric., Washington, DC, 1963.
7. C.S. James, and J.B. Wark Conveyance Estimation for Meandering Channels 1992 HR Wallingford Wallingford, UK (Rep. SR 329)
8. H.H. Barnes Jr., Roughness characteristics of natural channels, US Geological Survey Water-Supply Paper 1849, Federal Center, Colo., 1967.
9. R.D. Jarrett Hydraulics of high gradient streams J. Hydraulic Eng., ASCE 110 1984 1519 1539
10. K. Shiono, J.S. Al-Romaih, and D.W. Knight Stage-discharge assessment in compound meandering channels J. Hydraulic Eng., ASCE 125 1 1999 66 77
11. S. Jena, Stage-discharge relationship in simple meandering channels, M.Tech Thesis Submitted to IIT Kharagpur, 2006.
12. Lai Sai Hin, Nabil Bessaih, Law Puong Ling, Aminuddin Ab. Ghani, Nor Azazi Zakaria, and Mahyau Seng Discharge estimation for equatorial natural rivers with overbank flow Int. J. River Basin Manage., IAHR 6 1 2008 13 21
13. A.A. Maria, and A.F. DaSilva Friction factor of meandering flows J. Hydraulic Eng., ASCE 1259 7 1999 779 783
14. R.H.J. Sellin A laboratory investigation in to the interaction between the flow in the channel of a river and that over its flood plain Houille Blanche 20 7 1964 793 801
15. K.C. Patra, and S.K. Kar Flow interaction on meandering river with flood plains J. Hydraulic Eng., ASCE 126 8 2000 593 604 (Pubitemid 30629577)
16. K.C. Patra, and K.K. Khatua Energy loss and discharge estimation in two stage meandering and straight compound channel 2006 EWRI of ASCE and IIT Kanpur
17. M. Sahu, and K.K. Khatua Prediction of entrance length for low Reynolds number flow in pipe using neuro-fuzzy inference system Expert Syst. Appl. 39 2012 4545 4557
18. O. Kisi, T. Haktanir, M. Ardiclioglu, O. Ozturk, E. Yalcin, and S. Uludag Adaptive neuro-fuzzy computing technique for suspended sediment estimation Adv. Eng. Software 40 6 2009 438 444
19. A. Samandar A model of adaptive neural-based fuzzy inference system (ANFIS) for prediction of friction coefficient in open channel flow Sci. Res. Essays 6 5 2011 1020 1027
20. F.J. Chang, and Y.T. Chang Adaptive neuro-fuzzy inference system for prediction of water level in reservoir Adv. Water Resour. 29 2006 1 10
21. Hossien Riahi-Madvar, Seyed Ali Ayyoubzadeh, Ehsan Khadangi, and Mohammad Mehdi Ebadzadeh An expert system for predicting longitudinal dispersion coefficient in natural streams by using ANFIS Expert Syst. Appl. 36 2009 8589 8596
22. J.T. Limerions, Determination of the Manning coefficient from measured bed roughness in natural channels, in: Water Supply Paper 1898-B, US Geological Survey, Washington, DC, 1970.
23. B.B. Willetts, and R.I. Hardwick Stage dependency for overbank flow in meandering channels Proc. Inst. Civil Eng. Water Marit. Energy 101 1993 45 54
24. A.B. MacLeod, Development of methods to predict the discharge capacity in model and prototype meandering compound channel, Ph.D Thesis submitted to University of Glasgow, 1997.
25. R.H.J. Sellin, D.A. Ervine, and B.B. Willetts Behaviour of meandering two-stage channels Proc. Inst. Civil Eng.Water Marit. Energy 101 1993 99 111 (Pubitemid 24395682)
26. ASCE Task Committee, Artificial neural networks in hydrology, I: preliminary concepts, J. Hydrol. Eng., ASCE 2000; 5(2): 115-123.
27. T. Takagi, M. Sugeno. Derivation of fuzzy control rules from human operator's control actions, in: Proc. IFAC Symp. Fuzzy Inform., Knowledge Representation and Decision Analysis, 1983, pp. 55-60. (Pubitemid 14633040)
28. R.R. Yager, and L.A. Zadeh Fuzzy Sets Neural Networks, and Soft Computing 1994 Van Nostrand Reinhold
29. Manish Kumar, Devendra P. Garg, Intelligent learning of fuzzy logic controllers via neural network and genetic algorithm, in: Proceedings of JUSFA 2004 Japan - USA Symposium on Flexible Automation Denver, Colorado, July 19-21, 2004.
30. J.S.R. Jang ANFIS: adaptive-network-based fuzzy inference system IEEE Trans. Syst. Man Cybern. 23 3 1993 665 685
31. K.A. Dezfoli Principles of Fuzzy Theory and Its Application on Water Engineering Problems 2003 Jihad Press Tehran, Iran 227
32. L.A. Zadeh Outline of a new approach to the analysis of complex system and decision processes IEEE Trans. Syst., Man, Cybern. 3 1973 28 44
33. M. Tashnehlab, and S. Menhaj Modeling trip tours using ANFIS modeling Eng. J. Tehran Univ. 31 2001 361 370
34. Y.M. Wang, and Taha M.S. Elhag An adaptive neuro-fuzzy inference system for bridge risk assessment Expert Syst. Appl. 34 3 2008 3099 3106 (Pubitemid 351173813)
35. P.J. Werbos. Beyond regression: new tools for prediction and analysis in the behavioral sciences, PhD thesis, Harvard University, 1974.
36. M. Sadatpour, M. Afshar, and A. Hadad Fuzzy optimized model for river water quality modeling Isfahan J. Water Wastewater, Isfahan, Iran 64 2005 15 27
37. D.E. Rumelhart, G.E. Hinton, and R.J. Williams Learning internal representation by error propagation Parallel and Distributed Processing: Explorations in The Microstructure of Cognition Foundations vol. 1 1986 MIT Press Cambridge, MA