Explaining the internal behaviour of artificial neural network river flow models

A novel method of visualizing and understanding the internal functional behaviour of an artificial neural network (ANN) river flow model is presented. The method hypothesizes that an ANN is able to map a function similar to the flow duration curve while modelling the river flow. A mathematical analy...

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Bibliographic Details
Published in:Hydrological processes 2004-03, Vol.18 (4), p.833-844
Main Authors: Sudheer, K. P., Jain, Ashu
Format: Article
Language:English
Subjects:
artificial neural network
black-box models
Earth sciences
Earth, ocean, space
Exact sciences and technology
hydrologic models
Hydrology
Hydrology. Hydrogeology
rainfall-runoff models
surface hydrology
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Summary:A novel method of visualizing and understanding the internal functional behaviour of an artificial neural network (ANN) river flow model is presented. The method hypothesizes that an ANN is able to map a function similar to the flow duration curve while modelling the river flow. A mathematical analysis of the hypothesis is presented, and a case study of an ANN river flow model confirms its significance. The proposed approach is also useful within other models that improve the performance of an ANN. The reasons why these models improve a raw ANN can be clearly understood using this approach. While the field of ANN knowledge‐extraction is one that continues to attract considerable interest, it is anticipated that the current approach will initiate further research and make ANNs more useful to the hydrologic community. Copyright © 2004 John Wiley & Sons, Ltd.
ISSN:0885-6087
1099-1085
DOI:10.1002/hyp.5517