Journal of Earth System Science

, Volume 121, Issue 1, pp 125–133 | Cite as

Appraisal of soft computing techniques in prediction of total bed material load in tropical rivers

  • H MD AZAMATHULLAEmail author


This paper evaluates the performance of three soft computing techniques, namely Gene-Expression Programming (GEP) (Zakaria et al 2010), Feed Forward Neural Networks (FFNN) (Ab Ghani et al 2011), and Adaptive Neuro-Fuzzy Inference System (ANFIS) in the prediction of total bed material load for three Malaysian rivers namely Kurau, Langat and Muda. The results of present study are very promising: FFNN (R 2 = 0.958, RMSE = 0.0698), ANFIS (R 2 = 0.648, RMSE = 6.654), and GEP (R 2 = 0.97, RMSE = 0.057), which support the use of these intelligent techniques in the prediction of sediment loads in tropical rivers.


Alluvial channels Sediment transport River engineering ANN ANFIS GEP 


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Copyright information

© Indian Academy of Sciences 2012

Authors and Affiliations

    • 1
    • 1
    Email author
    • 1
    • 1
  1. 1.River Engineering and Urban Drainage Research Centre (REDAC)Universiti Sains MalaysiaPulau PinangMalaysia

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