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Hybrid neural network—finite element river flow model

journal contribution
posted on 01.01.2005, 00:00 authored by Lloyd ChuaLloyd Chua, K Holz
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.

History

Journal

Journal of hydrologic engineering

Volume

131

Issue

1

Pagination

52 - 59

Publisher

American Society of Civil Engineers

Location

Reston, Va.

ISSN

1943-5584

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal

Copyright notice

2005, American Society of Civil Engineers