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| Titel |
Legitimising data-driven models: exemplification of a new data-driven mechanistic modelling framework |
| VerfasserIn |
N. J. Mount, C. W. Dawson, R. J. Abrahart |
| 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 ; 17, no. 7 ; Nr. 17, no. 7 (2013-07-17), S.2827-2843 |
| Datensatznummer |
250018939
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| Publikation (Nr.) |
 copernicus.org/hess-17-2827-2013.pdf |
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| Zusammenfassung |
| In this paper the difficult problem of how to legitimise data-driven
hydrological models is addressed using an example of a simple artificial
neural network modelling problem. Many data-driven models in hydrology have
been criticised for their black-box characteristics, which prohibit adequate
understanding of their mechanistic behaviour and restrict their wider
heuristic value. In response, presented here is a new generic data-driven
mechanistic modelling framework. The framework is significant because it
incorporates an evaluation of the legitimacy of a data-driven model's
internal modelling mechanism as a core element in the modelling process. The
framework's value is demonstrated by two simple artificial neural network
river forecasting scenarios. We develop a novel adaptation of first-order
partial derivative, relative sensitivity analysis to enable each model's
mechanistic legitimacy to be evaluated within the framework. The results
demonstrate the limitations of standard, goodness-of-fit validation
procedures by highlighting how the internal mechanisms of complex models
that produce the best fit scores can have lower mechanistic legitimacy than
simpler counterparts whose scores are only slightly inferior. Thus, our
study directly tackles one of the key debates in data-driven, hydrological
modelling: is it acceptable for our ends (i.e. model fit) to justify our
means (i.e. the numerical basis by which that fit is achieved)? |
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