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Flood estimation at ungauged sites using artificial neural networks

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journal contribution
posted on 18.08.2006 by Christian Dawson, Robert J. Abrahart, Asaad Y. Shamseldin, Robert Wilby
Artificial neural networks (ANNs) have been applied within the field of hydrological modelling for over a decade but relatively little attention has been paid to the use of these tools for flood estimation in ungauged catchments. This paper uses data from the Centre for Ecology and Hydrology's Flood Estimation Handbook (FEH) to predict T-year flood events and the index flood (the median of the annual maximum series) for 850 catchments across the UK. When compared with multiple regression models, ANNs provide improved flood estimates that can be used by engineers and hydrologists. Comparisons are also made with the empirical model presented in the FEH and a preliminary study is made of the spatial distribution of ANN residuals, highlighting the influence that geographical factors have on model performance.

History

School

  • Social Sciences

Department

  • Geography and Environment

Pages

342460 bytes

Citation

DAWSON, C.W. ... et al, 2006. Flood estimation at ungauged sites using artificial neural networks. Journal of Hydrology, 319, pp.391-409.

Publisher

© Elsevier

Publication date

2006

Notes

This article was published in the journal, Journal of Hydrology [© Elsevier] and is also available at: http://www.sciencedirect.com/science/journal/00221694

ISSN

0022-1694

Language

en

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