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| Titel |
Multi-model data fusion for river flow forecasting: an evaluation of six alternative methods based on two contrasting catchments |
| VerfasserIn |
R. J. Abrahart, L. See |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 6, no. 4 ; Nr. 6, no. 4, S.655-670 |
| Datensatznummer |
250003664
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| Publikation (Nr.) |
 copernicus.org/hess-6-655-2002.pdf |
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| Zusammenfassung |
| This paper evaluates six published data
fusion strategies for hydrological forecasting based on two contrasting
catchments: the River Ouse and the Upper River Wye. The input level and
discharge estimates for each river comprised a mixed set of single model
forecasts. Data fusion was performed using: arithmetic-averaging, a
probabilistic method in which the best model from the last time step is used to
generate the current forecast, two different neural network operations and two
different soft computing methodologies. The results from this investigation are
compared and contrasted using statistical and graphical evaluation. Each
location demonstrated several options and potential advantages for using data
fusion tools to construct superior estimates of hydrological forecast. Fusion
operations were better in overall terms in comparison to their individual
modelling counterparts and two clear winners emerged. Indeed, the six different
mechanisms on test revealed unequal aptitudes for fixing different categories of
problematic catchment behaviour and, in such cases, the best method(s) were a
good deal better than their closest rival(s). Neural network fusion of
differenced data provided the best solution for a stable regime (with neural
network fusion of original data being somewhat similar) — whereas a fuzzified
probabilistic mechanism produced a superior output in a more volatile
environment. The need for a data fusion research agenda within the hydrological
sciences is discussed and some initial suggestions are presented.
Keywords: data fusion, fuzzy logic, neural network, hydrological modelling |
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