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

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...

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Bibliographic Details
Published in:Journal of Earth System Science 2012-02, Vol.121 (1), p.125-133
Main Authors: CHANG, C K, AZAMATHULLA, H MD, ZAKARIA, N A, GHANI, A AB
Format: Article
Language:English
Subjects:
Adaptive systems
Artificial neural networks
Bed load
Earth and Environmental Science
Earth science
Earth Sciences
Earth, ocean, space
Exact sciences and technology
Freshwater
Fuzzy logic
Gene expression
Neural networks
Predictions
Rivers
Sediment load
Sediment transport
Sedimentation & deposition
Soft computing
Space Exploration and Astronautics
Space Sciences (including Extraterrestrial Physics
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Summary: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.
ISSN:0253-4126
2347-4327
0973-774X
DOI:10.1007/s12040-012-0138-1