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Explaining the internal behaviour of artificial neural network river flow models
Author(s) -
Sudheer K. P.,
Jain Ashu
Publication year - 2004
Publication title -
hydrological processes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.222
H-Index - 161
eISSN - 1099-1085
pISSN - 0885-6087
DOI - 10.1002/hyp.5517
Subject(s) - artificial neural network , computer science , flow (mathematics) , field (mathematics) , function (biology) , artificial intelligence , current (fluid) , mathematical model , machine learning , geology , statistics , mathematics , oceanography , geometry , evolutionary biology , pure mathematics , biology
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.