Ideal point error for model assessment in data-driven river flow forecasting
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 diff...
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Copernicus Publications
2012-08-01
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Series: | Hydrology and Earth System Sciences |
Online Access: | http://www.hydrol-earth-syst-sci.net/16/3049/2012/hess-16-3049-2012.pdf |
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doaj-0697e276978a4fc38357206635f062b32020-11-24T23:11:14ZengCopernicus PublicationsHydrology and Earth System Sciences1027-56061607-79382012-08-011683049306010.5194/hess-16-3049-2012Ideal point error for model assessment in data-driven river flow forecastingC. W. DawsonN. J. MountR. J. AbrahartA. Y. ShamseldinWhen 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.http://www.hydrol-earth-syst-sci.net/16/3049/2012/hess-16-3049-2012.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
C. W. Dawson N. J. Mount R. J. Abrahart A. Y. Shamseldin |
spellingShingle |
C. W. Dawson N. J. Mount R. J. Abrahart A. Y. Shamseldin Ideal point error for model assessment in data-driven river flow forecasting Hydrology and Earth System Sciences |
author_facet |
C. W. Dawson N. J. Mount R. J. Abrahart A. Y. Shamseldin |
author_sort |
C. W. Dawson |
title |
Ideal point error for model assessment in data-driven river flow forecasting |
title_short |
Ideal point error for model assessment in data-driven river flow forecasting |
title_full |
Ideal point error for model assessment in data-driven river flow forecasting |
title_fullStr |
Ideal point error for model assessment in data-driven river flow forecasting |
title_full_unstemmed |
Ideal point error for model assessment in data-driven river flow forecasting |
title_sort |
ideal point error for model assessment in data-driven river flow forecasting |
publisher |
Copernicus Publications |
series |
Hydrology and Earth System Sciences |
issn |
1027-5606 1607-7938 |
publishDate |
2012-08-01 |
description |
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. |
url |
http://www.hydrol-earth-syst-sci.net/16/3049/2012/hess-16-3049-2012.pdf |
work_keys_str_mv |
AT cwdawson idealpointerrorformodelassessmentindatadrivenriverflowforecasting AT njmount idealpointerrorformodelassessmentindatadrivenriverflowforecasting AT rjabrahart idealpointerrorformodelassessmentindatadrivenriverflowforecasting AT ayshamseldin idealpointerrorformodelassessmentindatadrivenriverflowforecasting |
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