Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81532
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dc.contributor.authorRiahiMadvar, Hen_US
dc.contributor.authorDehghani, Men_US
dc.contributor.authorSeifi, Aen_US
dc.contributor.authorSalwana, Een_US
dc.contributor.authorShamshirband, Sen_US
dc.contributor.authorMosavi, Aen_US
dc.contributor.authorChau, KWen_US
dc.date.accessioned2019-10-28T05:45:57Z-
dc.date.available2019-10-28T05:45:57Z-
dc.date.issued2019-
dc.identifier.citationEngineering applications of computational fluid mechanics, 2019, v. 13, no. 1, p. 529-550en_US
dc.identifier.issn1994-2060-
dc.identifier.urihttp://hdl.handle.net/10397/81532-
dc.description.abstractThe 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.en_US
dc.description.sponsorshipDepartment of Civil and Environmental Engineeringen_US
dc.language.isoenen_US
dc.publisherHong Kong Polytechnic University, Department of Civil and Structural Engineeringen_US
dc.relation.ispartofEngineering applications of computational fluid mechanicsen_US
dc.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 (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en_US
dc.rightsThe 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 https://doi.org/10.1080/19942060.2019.1618396en_US
dc.subjectAlluvial channelsen_US
dc.subjectArtificial intelligenceen_US
dc.subjectBig dataen_US
dc.subjectGrade control structureen_US
dc.subjectRadial basis functionsen_US
dc.subjectScour geometryen_US
dc.titleComparative analysis of soft computing techniques RBF, MLP, and ANFIS with MLR and MNLR for predicting grade-control scour hole geometryen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage529-
dc.identifier.epage550-
dc.identifier.volume13-
dc.identifier.issue1-
dc.identifier.doi10.1080/19942060.2019.1618396-
dc.identifier.scopus2-s2.0-85069530236-
dc.identifier.eissn1997-003X-
dc.description.validate201910 bcma-
dc.description.oapublished_final-
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