Neural Computing and Applications

, Volume 24, Issue 2, pp 271–276 | Cite as

Development of GEP-based functional relationship for sediment transport in tropical rivers

Original Article

Abstract

This study presents gene expression programming (GEP), which is an extension to genetic programming (GP), as an alternative approach to modeling the functional relationships for the River Kurau, River Langat, and River Muda of the Malaysia. A functional relation has been developed using GEP with non-dimensional variables. The development of a GEP non-dimensional model is described. This paper compares current prediction equation with the existing GEP model for the same rivers (Zakaria et al. in Sci Total Environ 408:5078–5085, (2010). The presented model in this study is a less input GEP model and that predicts good performance. The proposed GEP approach gives satisfactory results compared to existing predictors.

Keywords

Malaysian Rivers Gene expression programming Sediment transport Regression analysis 

References

  1. 1.
    Ab. Ghani A (1993) Sediment transport in sewers. Ph.D. thesis, University of Newcastle upon Tyne, UKGoogle Scholar
  2. 2.
    Ab. Ghani A, Azamathulla HMd, Chang CK, Zakaria NA, Abu Hasan Z (2011) Prediction of total bed material load for rivers in Malaysia: a case study of Langat, Muda and Kurau Rivers. J Environ Fluid Mech 11(3):307–318CrossRefGoogle Scholar
  3. 3.
    Ab. Ghani A, Chang CK, Abdulla R, Zakaria NA (2003) Guidelines for field data collection and analysis of river sediment, Department of Irrigation and Drainage Malaysia, Kuala Lumpur, 35 pp. ISBN: 983-3067-03-4Google Scholar
  4. 4.
    Azamathulla HMd, Ab. Ghani A, Zakaria NA, Guven A (2010) Genetic programming to predict bridge pier scour. ASCE J Hydraul Eng 136(3):165–169CrossRefGoogle Scholar
  5. 5.
    Azamathulla HMd, Chang CK, Ab. Ghani AA, Zakaria NA, Ariffin J, Abu Hasan Z (2009) An ANFIS-based approach for predicting the bed load for moderately-sized rivers. J Hydro-Environ Res 3(1):35–44CrossRefGoogle Scholar
  6. 6.
    Bhattacharya B, Price RK, Solomatine DP (2007) Machine learning approach to modeling sediment transport. ASCE J Hydraul Eng 133(4):440–450CrossRefGoogle Scholar
  7. 7.
    DID (2009) Department of Irrigation and Drainage Malaysia or DID. Study on river sand mining capacity in MalaysiaGoogle Scholar
  8. 8.
    Ferreira C (2001) Gene expression programming in problem solving, 6th Online World Conference on Soft Computing in Industrial Applications (invited tutorial)Google Scholar
  9. 9.
    Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst 13(2):87–129MATHGoogle Scholar
  10. 10.
    Guven A, Aytek A (2009) A new approach for stage-discharge relationship: gene-expression programming. J Hydrol Eng 14(8):812–820CrossRefGoogle Scholar
  11. 11.
    Guven A, Gunal M (2008) Prediction of scour downstream of grade-control structures using neural networks. ASCE J Hydraul Eng 134(11):1656–1660CrossRefGoogle Scholar
  12. 12.
    Guven A, Gunal M (2008) Genetic programming approach for prediction of local scour downstream of hydraulic structures. ASCE J Irrig Drain Eng 134(2):241–249CrossRefGoogle Scholar
  13. 13.
    Guven A (2009) Linear genetic programming for time-series modelling of daily flow rate. J Earth Syst Sci 118(2):137–146CrossRefGoogle Scholar
  14. 14.
    Guven A, Aytek A, Yuce MI, Aksoy H (2008) Genetic programming-based empirical model for daily reference evapotranspiration model. Clean Soil Air Water 36(10–11):905–912CrossRefGoogle Scholar
  15. 15.
    Kisi O (2005) Suspended sediment estimation using neuro-fuzzy and neural network approaches. Hydrol Sci 50(4):683–696Google Scholar
  16. 16.
    Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. The MIT Press, CambridgeMATHGoogle Scholar
  17. 17.
    Nagy HM, Watanabe K, Hirano M (2002) Prediction of sediment load concentration in rivers using artificial neural network model. ASCE, J Hydraul Eng 128(6):588–595Google Scholar
  18. 18.
    Sasal EMD, Isik S (2005) Suspended sediment load estimation in lower Sakarya River by using soft computational methods. In: Proceeding of the international conference on computational and mathematical methods in science and engineering, CMMSE 2005, Alicante, Spain, pp 395–406Google Scholar
  19. 19.
    Yang et al (2009) Evaluation of total load sediment transport using ANN. Int J Sediment Res 24(3):274–286CrossRefGoogle Scholar
  20. 20.
    Zakaria NA, Azamathulla HMd, Chang CK, Ab. Ghani A (2010) Gene expression programming for total bed material load estimation—a case study. Sci Total Environ (STOTEN) 408:5078–5085CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2012

Authors and Affiliations

  1. 1.River Engineering and Urban Drainage Research Centre (REDAC)Engineering Campus, Universiti Sains MalaysiaNibong TebalMalaysia

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