Application of improved neuro-fuzzy GMDH to predict scour depth at sluice gates
An improved neuro-fuzzy based group method of data handling using the particle swarm optimization (NF-GMDH-PSO) is developed as an adaptive learning network to predict the localized scour downstream of a sluice gate with an apron. . The input characteristic parameters affecting the scour depth are t...
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sg-ntu-dr.10356-1017462020-03-07T11:45:53Z Application of improved neuro-fuzzy GMDH to predict scour depth at sluice gates Najafzadeh, Mohammad Lim, Siow Yong School of Civil and Environmental Engineering DRNTU::Engineering::Civil engineering::Water resources An improved neuro-fuzzy based group method of data handling using the particle swarm optimization (NF-GMDH-PSO) is developed as an adaptive learning network to predict the localized scour downstream of a sluice gate with an apron. . The input characteristic parameters affecting the scour depth are the sediment size and its gradation, apron length, sluice gate opening, and the flow conditions upstream and downstream of the sluice gate. Six non-dimensional parameters were yielded to define a functional relationship between the input and output variables. The training and testing of the NF-GMDH network are performed using published scour data from the literature. The efficiency of the training stages for the NF-GMDH-PSO is investigated. The testing results for the NF-GMDH network are compared with the traditional approaches based on regression method. A sensitivity analysis is carried out to assign the most significant parameter for the scour prediction. The results showed that the NF-GMDH-PSO network produced lower error in scour prediction than all other models. Accepted version 2014-06-12T05:58:37Z 2019-12-06T20:43:52Z 2014-06-12T05:58:37Z 2019-12-06T20:43:52Z 2014 2014 Journal Article Najafzadeh, M., & Lim, S. Y. (2014). Application of improved neuro-fuzzy GMDH to predict scour depth at sluice gates. Earth Science Informatics, in press. 1865-0473 https://hdl.handle.net/10356/101746 http://hdl.handle.net/10220/19694 10.1007/s12145-014-0144-8 en Earth science informatics © 2014 Springer-Verlag Berlin Heidelberg. This is the author created version of a work that has been peer reviewed and accepted for publication by Earth Science Informatics, Springer-Verlag Berlin Heidelberg. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: http://dx.doi.org/10.1007/s12145-014-0144-8. 26 p. application/pdf |
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DRNTU::Engineering::Civil engineering::Water resources Najafzadeh, Mohammad Lim, Siow Yong Application of improved neuro-fuzzy GMDH to predict scour depth at sluice gates |
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An improved neuro-fuzzy based group method of data handling using the particle swarm optimization (NF-GMDH-PSO) is developed as an adaptive learning network to predict the localized scour downstream of a sluice gate with an apron. . The input characteristic parameters affecting the scour depth are the sediment size and its gradation, apron length, sluice gate opening, and the flow conditions upstream and downstream of the sluice gate. Six non-dimensional parameters were yielded to define a functional relationship between the input and output variables. The training and testing of the NF-GMDH network are performed using published scour data from the literature. The efficiency of the training stages for the NF-GMDH-PSO is investigated. The testing results for the NF-GMDH network are compared with the traditional approaches based on regression method. A sensitivity analysis is carried out to assign the most significant parameter for the scour prediction. The results showed that the NF-GMDH-PSO network produced lower error in scour prediction than all other models. |
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School of Civil and Environmental Engineering |
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School of Civil and Environmental Engineering Najafzadeh, Mohammad Lim, Siow Yong |
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Article |
author |
Najafzadeh, Mohammad Lim, Siow Yong |
author_sort |
Najafzadeh, Mohammad |
title |
Application of improved neuro-fuzzy GMDH to predict scour depth at sluice gates |
title_short |
Application of improved neuro-fuzzy GMDH to predict scour depth at sluice gates |
title_full |
Application of improved neuro-fuzzy GMDH to predict scour depth at sluice gates |
title_fullStr |
Application of improved neuro-fuzzy GMDH to predict scour depth at sluice gates |
title_full_unstemmed |
Application of improved neuro-fuzzy GMDH to predict scour depth at sluice gates |
title_sort |
application of improved neuro-fuzzy gmdh to predict scour depth at sluice gates |
publishDate |
2014 |
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https://hdl.handle.net/10356/101746 http://hdl.handle.net/10220/19694 |
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1681041429254635520 |