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Christian Dawson
Christian
Dawson
Robert J. Abrahart
Robert J.
Abrahart
Asaad Y. Shamseldin
Asaad Y.
Shamseldin
Robert Wilby
Robert
Wilby
Flood estimation at ungauged sites using artificial neural networks
Loughborough University
2006
Artificial neural networks
Flood estimation
Ungauged catchments
Earth Sciences not elsewhere classified
2006-08-18 12:03:36
Journal contribution
https://repository.lboro.ac.uk/articles/journal_contribution/Flood_estimation_at_ungauged_sites_using_artificial_neural_networks/9402263
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.