Environmental Fluid Mechanics

, Volume 11, Issue 3, pp 307–318

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

DOI: 10.1007/s10652-010-9177-9

Cite this article as:
Ab. Ghani, A., Azamathulla, H.M., Chang, C.K. et al. Environ Fluid Mech (2011) 11: 307. doi:10.1007/s10652-010-9177-9

Abstract

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.

Keywords

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

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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