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

Journal of Earth system science

H Md Azamathulla1¹  N A Zakaria1¹  C K Chang1¹  A Ab Ghani1¹ 

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

关键词: Alluvial channels;    Sediment transport;    River engineering;    ANN;    ANFIS;    GEP.;   

DOI  :  

学科分类：天文学(综合)

来源: Indian Academy of Sciences

【 摘 要 】

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.

【 授权许可】

Unknown