Environmental Fluid Mechanics

, Volume 11, Issue 3, pp 307–318 | Cite as

Prediction of total bed material load for rivers in Malaysia: A case study of Langat, Muda and Kurau Rivers

  • Aminuddin Ab. Ghani
  • H. Md. Azamathulla
  • Chun Kiat Chang
  • Nor Azazi Zakaria
  • Zorkeflee Abu Hasan
Original Article


A soft computational technique is applied to predict sediment loads in three Malaysian rivers. The feed forward-back propagated (schemes) artificial neural network (ANNs) architecture is employed without any restriction to an extensive database compiled from measurements in Langat, Muda, Kurau different rivers. The ANN method demonstrated a superior performance compared to other traditional sediment-load methods. The coefficient of determination, 0.958 and the mean square error 0.0698 of the ANN method are higher than those of the traditional method. The performance of the ANN method demonstrates its predictive capability and the possibility of generalization of the modeling to nonlinear problems for river engineering applications.


Alluvial channels Artificial neural network Total-sediment load River engineering Sediment transport 


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  1. 1.
    Ab. Ghani A (1993) Sediment transport in sewers. PhD thesis, University of Newcastle upon Tyne, UKGoogle Scholar
  2. 2.
    Ab. Ghani A, Zakaria NA, Abdullah R, Mohd Sidek L (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
  3. 3.
    Ackers P, White WR (1973) Sediment transport: new approach and analysis. J Hydraul Div ASCE 99(HY11): 2041–2060Google Scholar
  4. 4.
    ASCE Task Committee (2000) Artificial neural networks in hydrology. I. Preliminary concepts. J Hydraul Eng ASCE 5(2):115–123Google Scholar
  5. 5.
    Azmathullah HMd, Deo MC, Deolalikar PB (2005) Neural networks for estimation of scour downstream of ski-jump bucket. J Hydraul Eng 131(10): 898–908CrossRefGoogle Scholar
  6. 6.
    Azmathullah HMd, Deo MC, Deolalikar PB (2006) Estimation of scour below spillways using neural networks. J Hydraul Res 44(1): 61–69CrossRefGoogle Scholar
  7. 7.
    Azamathulla HMd, Deo MC, Deolalikar PB (2008) Alternative neural networks to estimate the scour below spillways. Adv Eng Softw 39(8): 689–698CrossRefGoogle Scholar
  8. 8.
    Bowden GJ, Maier HR, Dandy GC (2002) Optimal division of data for neural network models in water resources applications. Water Resour Res 38(2): 1–11CrossRefGoogle Scholar
  9. 9.
    Cigizoglu HK (2002) Suspended sediment estimation for rivers using artificial neural networks and sediment rating curves. Turkish J Eng Env TUBITAK 26: 27–36Google Scholar
  10. 10.
    Cigizoglu HK (2004) Estimation and forecasting of daily suspended sediment data by multi-layer perceptrons. Adv Water Resour 27: 185–195CrossRefGoogle Scholar
  11. 11.
    Cigizoglu HK, Kisi O (2006) Methods to improve the neural network performance in suspended sediment estimation. J Hydrol 317: 221–238CrossRefGoogle Scholar
  12. 12.
    Dawson CW, Wilby R (1998) An artificial neural network approach to rainfall-runoff modeling. Hydrol Sci 43(1): 47–66CrossRefGoogle Scholar
  13. 13.
    Department of Irrigation and Drainage Malaysia or DID (2009) Study on river sand mining capacity in Malaysia. DID, Kuala lumpur, MalaysiaGoogle Scholar
  14. 14.
    Dogan E, Yuksel I, Kisi O (2007) Estimation of sediment concentration obtained by experimental study using artificial neural networks. Environ Fluid Mech 7: 271–288CrossRefGoogle Scholar
  15. 15.
    Engelund F, Hansen E (1967) A monograph on sediment transport in alluvial streams. Teknisk Forlag, Copenhagen, DenmarkGoogle Scholar
  16. 16.
    Haykin S (1994) Neural networks: a comprehensive foundation. MacMillan, New YorkGoogle Scholar
  17. 17.
    Hornik K, Stinchcombe M, White H (1989) Multilayer feed-forward networks are universal approximators. Neural Netw 2: 359–366CrossRefGoogle Scholar
  18. 18.
    Jain SK (2001) Development of integrated sediment rating curves using ANNs. J Hydraul Eng ASCE 127(1): 30–37CrossRefGoogle Scholar
  19. 19.
    Kisi O, Yuksel I, Dogan E (2008) Modelling daily suspended sediment of rivers in Turkey using several data driven techniques. Hydrol Sci J 53(6): 1270–1285CrossRefGoogle Scholar
  20. 20.
    Laursen EM (1958) The total sediment load of streams. J Hydraul Div ASCE 84(HY1): 1530-1–1530-6Google Scholar
  21. 21.
    Nagy HM, Watanabe K, Hirano M (2002) Prediction of sediment load concentration in rivers using artificial neural network model. J Hydraul Eng ASCE 128(6): 588–595CrossRefGoogle Scholar
  22. 22.
    Tayfur G, Guldal V (2006) Artificial neural networks for estimating daily total suspended sediment in natural streams. Nordic Hydrol 37(1): 69–79Google Scholar
  23. 23.
    Yang CT (1972) Unit stream power and sediment transport. J Hydraul Div ASCE 98(10): 523–567 proceeding paper 9295Google Scholar
  24. 24.
    Yang CT, Reza M, Aalami MT (2009) Evaluation of total load sediment transport using AAN. Int J Sediment Res 24(3): 274–286CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Aminuddin Ab. Ghani
    • 1
  • H. Md. Azamathulla
    • 1
  • Chun Kiat Chang
    • 1
  • Nor Azazi Zakaria
    • 1
  • Zorkeflee Abu Hasan
    • 1
  1. 1.River Engineering and Urban Drainage Research Centre (REDAC)Universiti Sains MalaysiaNibong TebalMalaysia

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