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| Titel |
Neural network modelling of non-linear hydrological relationships |
| VerfasserIn |
R. J. Abrahart, L. M. 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 ; 11, no. 5 ; Nr. 11, no. 5 (2007-09-20), S.1563-1579 |
| Datensatznummer |
250009468
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| Publikation (Nr.) |
 copernicus.org/hess-11-1563-2007.pdf |
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| Zusammenfassung |
| Two recent studies have suggested that neural network modelling offers no
worthwhile improvements in comparison to the application of weighted linear
transfer functions for capturing the non-linear nature of hydrological
relationships. The potential of an artificial neural network to perform
simple non-linear hydrological transformations under controlled conditions
is examined in this paper. Eight neural network models were developed: four
full or partial emulations of a recognised non-linear hydrological
rainfall-runoff model; four solutions developed on an identical set of
inputs and a calculated runoff coefficient output. The use of different
input combinations enabled the competencies of solutions developed on
a reduced number of parameters to be assessed. The selected hydrological
model had a limited number of inputs and contained no temporal component.
The modelling process was based on a set of random inputs that had a uniform
distribution and spanned a modest range of possibilities. The initial
cloning operations permitted a direct comparison to be performed with the
equation-based relationship. It also provided more general information about
the power of a neural network to replicate mathematical equations and model
modest non-linear relationships. The second group of experiments explored a
different relationship that is of hydrological interest; the target surface contained
a stronger set of non-linear properties and was more challenging.
Linear modelling comparisons were performed against traditional least
squares multiple linear regression solutions developed on identical
datasets. The reported results demonstrate that neural networks are capable
of modelling non-linear hydrological processes and are therefore appropriate
tools for hydrological modelling. |
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