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An expert system for predicting Manning’s roughness coefficient in open channels by using gene expression programming

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Abstract

Manning’s roughness coefficient (n) has been widely used in the estimation of flood discharges or depths of flow in natural channels. Accurate estimation of Manning’s roughness coefficient is essential for the computation of flow rate, velocity. Conventional formulae that are greatly based on empirical methods lack in providing high accuracy for the prediction of Manning’s roughness coefficient. Consequently, new and accurate techniques are still highly demanded. In this study, gene expression programming (GEP) is used to estimate the Manning’s roughness coefficient. The estimated value of the roughness coefficient is used in Manning’s equation to compute the flow parameters in open-channel flows in order to carry out a comparison between the proposed GEP-based approach and the conventional ones. Results show that computed discharge using estimated value of roughness coefficient by GEP is in good agreement (±10%) with the experimental results compared to the conventional formulae (R 2 = 0.97 and RMSE = 0.0034 for the training data and R 2 = 0.94 and RMSE = 0.086 for the testing data).

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References

  1. Ab. Ghani A, Zakaria NA, Chang CK, Ariffin J, Abu Hasan Z, Abdul Ghaffar AB (2007) Revised equations for Manning’s coefficient for sand-bed rivers. Int J River Basin Manag 5(4):329–346

  2. Alavi AH, Ameri M, Gandomi AH, Mirzahosseini MR (2011) Formulation of flow number of asphalt mixes using a hybrid computational method. Constr Build Mater 25(3):1338–1355

    Article  Google Scholar 

  3. Altun H, Bilgil A, Fidan CF (2006) Treatment of multi-dimensional data to enhance neural network estimators in regression problems. Expert Syst Appl 32(2):599–605

    Article  Google Scholar 

  4. Azamathulla HMd, Ahmad Z (2012) Gene-expression programming for transverse mixing coefficient. J Hydrol 434–435:142–148

    Article  Google Scholar 

  5. Azamathulla HMd, Deo MC, Deolalikar PB (2008) Alternative neural networks to estimate the scour below spillways. Adv Eng Softw 39(8):689–698

    Article  Google Scholar 

  6. Barnes HH Jr (1967) Roughness characteristics of natural channels. US Geological Survey Water-Supply Paper 1949

  7. Bilgil A (1998) The effect of wall shear stress on friction factor in smooth open channel flows. Ph.D. Thesis. Karadeniz Technical University, Trabzon (Turkey) (in Turkish)

  8. Bilgil A, Altun H (2008) Investigation of flow resistance in smooth open channels using artificial neural networks. Flow Meas Instrum 19:404–408

    Article  Google Scholar 

  9. Çiray, C. (1999) 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

  10. Chow VT (1959) Open channel hydraulics. McGraw-Hill Book Co, New York

    Google Scholar 

  11. Dingman SL, Sharma KP (1997) Statistical development and validation of discharge equations for natural channels. J Hydrol 199:13–35

    Article  Google Scholar 

  12. Dooge JCI (1991) The Manning formula in context channel flow resistance; centennial of Manning’s formula. Water Resources Publications, Littleton (Colo), pp 136–185

    Google Scholar 

  13. Ferreira C (2001) Gene expression programming in problem solving. In: 6th online world conference on soft computing in industrial applications (invited tutorial)

  14. Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst 13(2):87–129

    MATH  Google Scholar 

  15. French RH (1985) Open channel hydraulics. McGraw-Hill, New York

    Google Scholar 

  16. Gandomi AH, Alavi AH, Mirzahosseini MR, Moqhadas Nejad F (2011) Nonlinear genetic-based models for prediction of flow number of asphalt mixtures. J Mater Civ Eng 23(3):248–263

    Article  Google Scholar 

  17. Gandomi AH, Babanajad SK, Alavi AH, Farnam Y (2012) A novel approach to strength modeling of concrete under triaxial compression. J Mater Civil Eng (in press). doi:10.1061/(ASCE)MT.1943-5533.0000494

  18. Gandomi AH, Tabatabaie SM, Moradian MH, Radfar A, Alavi AH (2011) A new prediction model for load capacity of castellated steel beams. J Constr Steel Res 67(7):1096–1105

    Article  Google Scholar 

  19. Gandomi AH, Alavi AH (2011) Multi-stage genetic programming: a new strategy to nonlinear system modeling. Inf Sci 181(23):5227–5239

    Article  Google Scholar 

  20. GEPSOFT (2006) GeneXproTools. Version 4.0. http://www.gepsoft.com

  21. Giustolisi O (2004) Using genetic programming to determine Chèzy resistance coefficient in corrugated channels. J Hydroinform 6(3):157–173

    Google Scholar 

  22. Green JC (2006) Effect of macrophyte spatial variability on channel resistance. Adv Water Resour 29:426–438

    Article  Google Scholar 

  23. Guven A, Aytek A (2009) A new approach for stage-discharge relationship: gene-expression programming. J. Hydrologic Eng 14(8):812–820

    Article  Google Scholar 

  24. Guven A, Gunal M (2008a) Genetic programming approach for prediction of local scour downstream of hydraulic structures. J Irrig Drain Eng 134(2):241–249

    Google Scholar 

  25. Guven A, Gunal M (2008b) Prediction of scour downstream of grade-control structures using neural networks. J Hydraul Eng 134(11):1656–1660

    Google Scholar 

  26. Henderson FM (1966) Open channel flow. MacMillan Co, New York

    Google Scholar 

  27. Hicks DM, Mason PD (1991) Roughness characteristics of New Zealand rivers. Water Resources Survey, Wellington

    Google Scholar 

  28. Jarrett RD (1984) Hydraulics of high gradient streams. ASCE J Hydraul Eng 110:1519–1539

    Article  Google Scholar 

  29. Jiang M, Li L-X (2010) An improved two-point velocity method for estimating the roughness coefficient of natural channels. Phys Chem Earth Parts A/B/C 35(3–5):182–186

    Article  Google Scholar 

  30. Kazemipour AK, Apelt CJ (1982) Shape effects on resistance to uniform flow in open channels. J Hydraul Res 17:129–147

    Article  Google Scholar 

  31. Keulegan GH (1938) Laws of turbulent flow in open channels. J Natl Bureau Stand 1151(21):707–741

    Article  Google Scholar 

  32. Koza JR (1999) Genetic programming: on the programming of computers by means of natural selection. The MIT Press, Cambridge, MA

    Google Scholar 

  33. Riggs HC (1976) A simplified slope area method for estimating flood discharges in natural channels. J Res US Geol Survey 4:285–291

    Google Scholar 

  34. Solomatine DP, Otsfeld A (2008) Data-driven modelling: some past experiences and new approaches. J Hydroinform 10(1):3–22

    Google Scholar 

  35. Myers WRC (1982) Flow resistance in wide rectangular channels. J Hydraul Div ASCE 108:471–482

    Google Scholar 

  36. Powell RW (1970) Resistance studies on smooth open channels. J Hydraul Div ASCE 364–367

    Google Scholar 

  37. Pillia NN (1970) On uniform flow through smooth rectangular open channels. J Hydraul Res 8:403–417

    Article  Google Scholar 

  38. Pillia NN (1997) Effect of shape on uniform flow through smooth rectangular open channels. Journal of Hydraulics Research 8:655–658

    Google Scholar 

  39. Rahman HK, Williams JR, Wormleaton PR (1997) Identification problem of open channel friction parameters. J Hydraul Res 8:1078–1088

    Google Scholar 

  40. Rao KK (1969) Effect of shape on the mean-flow characteristics of turbulent flow through smooth rectangular open channel. PhD Thesis presented to the University of Iowa at Iowa City, Iowa

  41. Reinus E (1961) Steady uniform flow in open channels transactions. Royal Institute Technology, Stockholm, p 179

    Google Scholar 

  42. Syamala P (1998) Velocity shear and friction studies in smooth rectangular channels of small aspect ratio for supercritical flows, PhD Thesis presented to the Indian Institute of Science at Bangalore, India

  43. Tinkler KJ (1997) Critical flow in rockbed streams with estimated values for Manning’s n. Geomorphology 20:147–164

    Article  Google Scholar 

  44. Tracy HJ, Lester CM (1961) Resistance coefficients and velocity distribution-smooth rectangular channel. US Geol Surv Water Supply Paper 1592-A, pp 1–18

  45. Wilson C (2007) Flow resistance models for flexible submerged vegetation. J Hydrol 342(3–4):1213–1222

    Google Scholar 

  46. Yen BC (2002) Open channel flow resistance. J Hydraul Eng 128:20–39

    Article  Google Scholar 

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Acknowledgments

The authors wish to thank Dr. Halis Altun, Coordinator of Intelligent Signal Processing and Interface Technologies Laboratory Embedded System Laboratory, Nigde University Engineering Faculty, Department of Electrical and Electronic Engineering, 51245, Kampus, Nigde, Turkey, for providing the data and suggestions on preparation of this manuscript.

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Authors and Affiliations

  1. River Engineering and Urban Drainage Research Centre (REDAC), Universiti Sains Malaysia, Engineering Campus, Seri Ampangan, 14300, Nibong Tebal, Pulau Pinang, Malaysia

    H. Md. Azamathulla & Aminuddin Ab. Ghani

  2. Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, 247667, India

    Z. Ahmad

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  1. H. Md. Azamathulla
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  2. Z. Ahmad
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  3. Aminuddin Ab. Ghani
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Correspondence to H. Md. Azamathulla.

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Azamathulla, H.M., Ahmad, Z. & Ab. Ghani, A. An expert system for predicting Manning’s roughness coefficient in open channels by using gene expression programming. Neural Comput & Applic 23, 1343–1349 (2013). https://doi.org/10.1007/s00521-012-1078-z

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