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

  • C K CHANG
  • H MD AZAMATHULLA
  • N A ZAKARIA
  • A AB GHANI
Article

Abstract

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 (R2 = 0.958, RMSE = 0.0698), ANFIS (R2 = 0.648, RMSE = 6.654), and GEP (R2 = 0.97, RMSE = 0.057), which support the use of these intelligent techniques in the prediction of sediment loads in tropical rivers.

Keywords

Alluvial channels Sediment transport River engineering ANN ANFIS GEP 

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Copyright information

© Indian Academy of Sciences 2012

Authors and Affiliations

  • C K CHANG
    • 1
  • H MD AZAMATHULLA
    • 1
  • N A ZAKARIA
    • 1
  • A AB GHANI
    • 1
  1. 1.River Engineering and Urban Drainage Research Centre (REDAC)Universiti Sains MalaysiaPulau PinangMalaysia

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