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    • Keywords


      Alluvial channels; Sediment transport; River engineering; ANN; ANFIS; GEP.

    • 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 (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.

    • Author Affiliations


      C K Chang1 H Md Azamathulla1 N A Zakaria1 A Ab Ghani1

      1. River Engineering and Urban Drainage Research Centre (REDAC), Universiti Sains Malaysia 14300 Nibong Tebal, Pulau Pinang, Malaysia.
    • Dates

  • Journal of Earth System Science | News

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      Posted on July 25, 2019

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