Skip to main content
Log in

Gene-expression programming to predict friction factor for Southern Italian rivers

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

This briefing article presents gene-expression programming (GEP), which is an extension to genetic programming, as an alternative approach to predict friction factor for Southern Italian rivers. Published data were compiled for the friction for 43 gravel-bed rivers of Calabria. The proposed GEP approach produces satisfactory results (R 2 = 0.958 and RMSE = 0.079) compared with existing predictors.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Alavi AH, Gandomi AH (2011) A robust data mining approach for formulation of geotechnical engineering systems. Eng Comput 28(3):242–274

    Google Scholar 

  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. Azamathulla HMd, Ghani AA, Zakaria NA, Guven A (2010) Genetic programming to predict bridge pier scour. ASCE J Hydraul Eng 136(3):165–169

    Article  Google Scholar 

  4. Bathurst JC, Li R, Simons DB (1981) Resistance equation for large-scale roughness. J Hydraul Div ASCE 107(HY12):1593–1613

    Google Scholar 

  5. Bray DI (1979) Estimating average velocity in gravel-bed rivers. J Hydraul Div ASCE 105(HY12):1103–1122

    Google Scholar 

  6. Colosimo C, Vito AC, Veltri M (1988) Friction factor evaluation in gravel-bed rivers. J Hydraul Eng 114(8):861–876

    Article  Google Scholar 

  7. Ferreira C (2001) Gene expression programming in problem solving. 6th Online world conference on soft computing in industrial applications (invited tutorial)

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

    MATH  Google Scholar 

  9. 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 ASCE 23(3):248–263

    Article  Google Scholar 

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

  11. 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 

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

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

    Google Scholar 

  14. Graf WH, Cao HH, Suszka L (1983) Hydraulics of steep mobile-bed channels. In: Proceedings of 20th congress of the international association for hydraulic research, Moscow, USSR

  15. Griffiths GA (1981) F low resistance in coarse gravel-bed rivers. J Hydraul Div ASCE 107(HY7):899–916

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  19. Hey RD (1979) Flow resistance in gravel-bed rivers. J Hydraul Div ASCE 105(HY4):365–379

    Google Scholar 

  20. Jarrett RD (1994) Historic-flood evaluation and research needs in mountainous areas. In: Cotroneo GV, Rumer RR (eds) Hydraulic engineering—proceedings of the symposium sponsored by the American Society of Civil Engineers, Buffalo, NY, 1–5 Aug 1994. American Society of Civil Engineers, New York, pp 875–879

  21. Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. A Bradford book. MIT Press, Cambridge

    Google Scholar 

  22. Kumar B (2011) Flow resistance in alluvial channel. Water Resour 38(6):745–754

    Google Scholar 

  23. Kumar B, Rao AR (2010) Metamodeling approach to predict friction factor of alluvial channel. Comput Electron Agric 70(1):144–150

    Article  Google Scholar 

  24. Kumar B, Bhatla A (2010) Genetic algorithm optimized neural network prediction of the friction factor in a mobile bed channel. J Intell Syst 19(4):315–335

    Google Scholar 

  25. Limerinos JT (1970) Determination of the Manning coefficient from measured bed roughness in natural channels. Water supply paper 1898-B, USGS, Washington, DC

  26. Marchi E (1961) II moto uniforme delle correnti liquid nei condotti chiusi e aperti. L’ Energia Elettrica, Milano, Italy, No. 4–5, 289–301 (Italian)

  27. Motamedi A, Afzalimehr H, Singh VP (2010) Estimation of friction factor in open channels. J Hydrol Eng 15(3):249–254

    Article  Google Scholar 

  28. Peterson DF, Mohanty PK (1960) Flume studies of flow in steep rough channels. J Hydraul Div ASCE 86(HY9):55–76

    Google Scholar 

  29. Rouse H (1965) Critical analysis of open-channel resistance. J Hydraul Div ASCE 91(HY4):1–25

    Google Scholar 

  30. Teodorescu L, Sherwood D (2008) High energy physics event selection with gene expression programming. Comput Phys Commun 178(6):409–419

    Google Scholar 

  31. Yalin MS (1972) Mechanics of sediment transport, 1st edn. Pergamon Press, New York

    Google Scholar 

  32. Yarahamadi MB, Fathi-Moghadam M, Bajestan MS (2010) Effects of land slope and flow depth on retarding flow in gravel-bed lands. World Appl Sci J 8(8):943–947

    Google Scholar 

  33. Yen BC (2002) Open channel flow resistance. J Hydraul Eng 128(1):20–39

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to H. Md. Azamathulla.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Md. Azamathulla, H. Gene-expression programming to predict friction factor for Southern Italian rivers. Neural Comput & Applic 23, 1421–1426 (2013). https://doi.org/10.1007/s00521-012-1091-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-012-1091-2

Keywords

Navigation