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Title: Comparative analysis of soft computing techniques RBF, MLP, and ANFIS with MLR and MNLR for predicting grade-control scour hole geometry
Authors: RiahiMadvar, H
Dehghani, M
Seifi, A
Salwana, E
Shamshirband, S
Mosavi, A
Chau, KW 
Keywords: Alluvial channels
Artificial intelligence
Big data
Grade control structure
Radial basis functions
Scour geometry
Issue Date: 2019
Publisher: Hong Kong Polytechnic University, Department of Civil and Structural Engineering
Source: Engineering applications of computational fluid mechanics, 2019, v. 13, no. 1, p. 529-550 How to cite?
Journal: Engineering applications of computational fluid mechanics 
Abstract: The main aims and contributions of the present paper are to use new soft computing methods for the simulation of scour geometry (depth/height and locations) in a comparative framework. Five models were used for the prediction of the dimension and location of the scour pit. The five developed models in this study are multilayer perceptron (MLP) neural network, radial basis functions (RBF) neural network, adaptive neuro fuzzy inference systems (ANFIS), multiple linear regression (MLR), and multiple non-linear regression (MNLR) in comparison with empirical equations. Four non-dimensional geometry parameters of scour hole shape are predicted by these models including the maximum scour depth (S), the distance of S from the weir (XS), the maximum height of downstream deposited sediments (hd), and distance of hd from the weir (XD). The best results over train data derived for XS/Z and hd/Z by the MLP model with R2 are 0.95 and 0.96 respectively; the best predictions for S/Z and XD/Z are from the ANFIS model with R2 0.91 and 0.96 respectively. The results indicate that the application of MLP and ANFIS results in the accurate prediction of scour geometry for the designing of stable grade control structures in alluvial irrigation channels.
ISSN: 1994-2060
EISSN: 1997-003X
DOI: 10.1080/19942060.2019.1618396
Rights: © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The following publication Hossien Riahi-Madvar, Majid Dehghani, Akram Seifi, Ely Salwana, Shahaboddin Shamshirband, Amir Mosavi & Kwok-wing Chau (2019) Comparative analysis of soft computing techniques RBF, MLP, and ANFIS with MLR and MNLR for predicting grade-control scour hole geometry, Engineering Applications of Computational Fluid Mechanics, 13:1, 529-550, is available at
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