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| Titel |
Data compression to define information content of hydrological time series |
| VerfasserIn |
S. V. Weijs, N. Giesen, M. B. Parlange |
| 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. 8 ; Nr. 17, no. 8 (2013-08-06), S.3171-3187 |
| Datensatznummer |
250085908
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| Publikation (Nr.) |
 copernicus.org/hess-17-3171-2013.pdf |
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| Zusammenfassung |
When inferring models from hydrological data or calibrating hydrological
models, we are interested in the information content of those data
to quantify how much can potentially be learned from them. In this
work we take a perspective from (algorithmic) information theory,
(A)IT, to discuss some underlying issues regarding this question.
In the information-theoretical framework, there is a strong link between
information content and data compression. We exploit this by using
data compression performance as a time series analysis tool and highlight
the analogy to information content, prediction and learning (understanding
is compression). The analysis is performed on time series of a set
of catchments.
We discuss both the deeper foundation from algorithmic information
theory, some practical results and the inherent difficulties in answering
the following question: "How much information is contained in this data set?".
The conclusion is that the answer to this question can only be given
once the following counter-questions have been answered: (1) information
about which unknown quantities? and (2) what is your current state of
knowledge/beliefs about those quantities?
Quantifying information content of hydrological data is closely linked to the
question of separating aleatoric and epistemic uncertainty and quantifying
maximum possible model performance, as addressed in the current hydrological
literature. The AIT perspective teaches us that it is impossible to answer
this question objectively without specifying prior beliefs. |
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