Application of improved neuro-fuzzy GMDH to predict scour depth at sluice gates
Lim, Siow Yong
Date of Issue2014
School of Civil and Environmental Engineering
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.
DRNTU::Engineering::Civil engineering::Water resources
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.