Flood estimation at ungauged sites using artificial neural networks
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...
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| Veröffentlicht in: | Journal of hydrology (Amsterdam) 2006-03, Vol.319 (1), p.391-409 |
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| container_title | Journal of hydrology (Amsterdam) |
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| creator | Dawson, C.W. Abrahart, R.J. Shamseldin, A.Y. Wilby, R.L. |
| description | 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. |
| doi_str_mv | 10.1016/j.jhydrol.2005.07.032 |
| format | Article |
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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. 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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.</abstract><pub>Elsevier B.V</pub><doi>10.1016/j.jhydrol.2005.07.032</doi><tpages>19</tpages><oa>free_for_read</oa></addata></record> |
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| language | eng |
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| subjects | Artificial neural networks estimation Flood estimation floods Freshwater hydrologic models neural networks Ungauged catchments watershed hydrology watersheds |
| title | Flood estimation at ungauged sites using artificial neural networks |
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