On the encapsulation of numerical-hydraulic models in artificial neural network

The optimal control of hydraulic networks often necessitates making a considerable number of very rapid simulations of flows, such as is not practical using existing, computationally-demanding, numerical-hydraulic models. However, the site-specific knowledge and data that is encapsulated in any such...

Full description

Saved in:
Bibliographic Details
Published in:Journal of hydraulic research 1999-01, Vol.37 (2), p.147-161
Main Authors: Dibike, Yonas B., Solomatine, Dimitri, abbott, Michael B.
Format: Article
Language:English
Subjects:
Computational fluid dynamics
Earth sciences
Earth, ocean, space
Engineering and environment geology. Geothermics
Engineering geology
Exact sciences and technology
Hydrology
Hydrology. Hydrogeology
Mathematical models
Neural networks
Optimal control systems
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The optimal control of hydraulic networks often necessitates making a considerable number of very rapid simulations of flows, such as is not practical using existing, computationally-demanding, numerical-hydraulic models. However, the site-specific knowledge and data that is encapsulated in any such numerical model can be encapsulated in its turn in an artificial neural network (ANN), and this can provide much faster simulations. In this study, a number of possible types and configurations of ANNs are investigated for their suitability to this class of application. When regarded from a hydroinformatics point of view, this study becomes one of identifying the most suitable ANN encapsulations of numerical-hydraulic encapsulations of generic hydraulic knowledge and site-specific data.
ISSN:0022-1686
1814-2079
DOI:10.1080/00221689909498303