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Hybrid neural network—finite element river flow model

Chua, Lloyd H.C. and Holz, K.P. 2005, Hybrid neural network—finite element river flow model, Journal of hydrologic engineering, vol. 131, no. 1, pp. 52-59, doi: 10.1061/(ASCE)0733-9429(2005)131:1(52).

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Title Hybrid neural network—finite element river flow model
Author(s) Chua, Lloyd H.C.ORCID iD for Chua, Lloyd H.C. orcid.org/0000-0003-2523-3735
Holz, K.P.
Journal name Journal of hydrologic engineering
Volume number 131
Issue number 1
Start page 52
End page 59
Total pages 8
Publisher American Society of Civil Engineers
Place of publication Reston, Va.
Publication date 2005
ISSN 1943-5584
Keyword(s) neural networks
hybrid methods
finite element method
hydraulic models
dikes
river flow
boundary conditions
Summary Results obtained from a hybrid neural network—finite element model are reported in this paper. The hybrid model incorporates artificial neural network (ANN) nodes into a numerical scheme, which solves the two-dimensional shallow water equations using finite elements (FE). First, numerical computations are carried out on the entire numerical model, using a larger mesh. The results from this computation are then used to train several preselected ANN nodes. The ANN nodes model the response for a part of the entire numerical model by transferring the system reaction to the location where both models are connected in real time. This allows a smaller mesh to be used in the hybrid ANN-FE model, resulting in savings in computation time. The hybrid model was developed for a river application, using the computational nodes located at the open boundaries to be the ANN nodes for the ANN-FE hybrid model. Real-time coupling between the ANN and FE models was achieved, and a reduction is CPU time of more than 25% was obtained.
Language eng
DOI 10.1061/(ASCE)0733-9429(2005)131:1(52)
Field of Research 099999 Engineering not elsewhere classified
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2005, American Society of Civil Engineers
Persistent URL http://hdl.handle.net/10536/DRO/DU:30063765

Document type: Journal Article
Collection: School of Engineering
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