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| Titel |
Why hydrological predictions should be evaluated using information theory |
| VerfasserIn |
S. V. Weijs, G. Schoups, N. Giesen |
| 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 ; 14, no. 12 ; Nr. 14, no. 12 (2010-12-13), S.2545-2558 |
| Datensatznummer |
250012530
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| Publikation (Nr.) |
 copernicus.org/hess-14-2545-2010.pdf |
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| Zusammenfassung |
| Probabilistic predictions are becoming increasingly popular in hydrology. Equally important
are methods to test such predictions, given the topical debate on uncertainty analysis in
hydrology.
Also in the special case of hydrological forecasting, there is still discussion about
which scores to use for their evaluation. In this paper, we propose to use information theory
as the central framework to evaluate predictions.
From this perspective, we hope to shed some light on what verification scores measure
and should measure.
We start from the ''divergence score'', a relative entropy measure that was
recently found to be an appropriate measure for forecast quality. An interpretation
of a decomposition of this measure provides insight in additive relations between
climatological uncertainty, correct information, wrong information and remaining uncertainty.
When the score is applied to deterministic forecasts,
it follows that these increase uncertainty to infinity. In practice, however,
deterministic forecasts tend to be judged far more mildly and are widely used.
We resolve this paradoxical result by proposing that deterministic forecasts
either are implicitly probabilistic or are implicitly evaluated with an underlying
decision problem or utility in mind.
We further propose that calibration of models representing a hydrological system should
be the based on information-theoretical scores, because this allows extracting all
information from the observations and avoids learning from information that is not there.
Calibration based on maximizing
utility for society trains an implicit decision model rather than the forecasting system
itself. This inevitably results in a loss
or distortion of information in the data and more risk of overfitting, possibly leading
to less valuable and
informative forecasts. We also show this in an example.
The final conclusion is that models should preferably be explicitly
probabilistic and calibrated to maximize the information they provide. |
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