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| Titel |
Towards model evaluation and identification using Self-Organizing Maps |
| VerfasserIn |
M. Herbst, M. C. Casper |
| 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 ; 12, no. 2 ; Nr. 12, no. 2 (2008-04-09), S.657-667 |
| Datensatznummer |
250010584
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| Publikation (Nr.) |
 copernicus.org/hess-12-657-2008.pdf |
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| Zusammenfassung |
| The reduction of information contained in model time series through the use
of aggregating statistical performance measures is very high compared to the
amount of information that one would like to draw from it for model
identification and calibration purposes. It has been readily shown that this
loss imposes important limitations on model identification and -diagnostics
and thus constitutes an element of the overall model uncertainty. In this
contribution we present an approach using a Self-Organizing Map (SOM) to
circumvent the identifiability problem induced by the low discriminatory
power of aggregating performance measures. Instead, a Self-Organizing Map is
used to differentiate the spectrum of model realizations, obtained from
Monte-Carlo simulations with a distributed conceptual watershed model, based
on the recognition of different patterns in time series. Further, the SOM is
used instead of a classical optimization algorithm to identify those model
realizations among the Monte-Carlo simulation results that most closely
approximate the pattern of the measured discharge time series. The results
are analyzed and compared with the manually calibrated model as well as with
the results of the Shuffled Complex Evolution algorithm (SCE-UA). In our
study the latter slightly outperformed the SOM results. The SOM method,
however, yields a set of equivalent model parameterizations and therefore
also allows for confining the parameter space to a region that closely
represents a measured data set. This particular feature renders the SOM
potentially useful for future model identification applications. |
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