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| Titel |
Ideal point error for model assessment in data-driven river flow forecasting |
| VerfasserIn |
C. W. Dawson, N. J. Mount, R. J. Abrahart, A. Y. Shamseldin |
| 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 ; 16, no. 8 ; Nr. 16, no. 8 (2012-08-29), S.3049-3060 |
| Datensatznummer |
250013445
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| Publikation (Nr.) |
 copernicus.org/hess-16-3049-2012.pdf |
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| Zusammenfassung |
| When analysing the performance of hydrological models in river forecasting,
researchers use a number of diverse statistics. Although some statistics
appear to be used more regularly in such analyses than others, there is a
distinct lack of consistency in evaluation, making studies undertaken by
different authors or performed at different locations difficult to compare
in a meaningful manner. Moreover, even within individual reported case
studies, substantial contradictions are found to occur between one measure
of performance and another. In this paper we examine the ideal point error
(IPE) metric – a recently introduced measure of model performance that
integrates a number of recognised metrics in a logical way. Having a single,
integrated measure of performance is appealing as it should permit more
straightforward model inter-comparisons. However, this is reliant on a
transferrable standardisation of the individual metrics that are combined to
form the IPE. This paper examines one potential option for standardisation:
the use of naive model benchmarking. |
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