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 dif...
| Main Authors: | , , , |
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| Format: | Article |
| Published: |
European Geosciences Union (EGU)
2012
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| Online Access: | https://eprints.nottingham.ac.uk/28051/ |
| _version_ | 1848793495489216512 |
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| author | Dawson, C.W Mount, Nick J. Abrahart, R.J Shamseldin, A.Y. |
| author_facet | Dawson, C.W Mount, Nick J. Abrahart, R.J Shamseldin, A.Y. |
| author_sort | Dawson, C.W |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-14T19:01:12Z |
| format | Article |
| id | nottingham-28051 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:01:12Z |
| publishDate | 2012 |
| publisher | European Geosciences Union (EGU) |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-280512020-05-04T20:22:24Z https://eprints.nottingham.ac.uk/28051/ Ideal point error for model assessment in data-driven river flow forecasting Dawson, C.W Mount, Nick J. Abrahart, R.J Shamseldin, A.Y. 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. European Geosciences Union (EGU) 2012 Article PeerReviewed Dawson, C.W, Mount, Nick J., Abrahart, R.J and Shamseldin, A.Y. (2012) Ideal point error for model assessment in data-driven river flow forecasting. Hydrology and Earth System Sciences, 16 (8). pp. 3049-3060. ISSN 1027-5606 http://www.hydrol-earth-syst-sci.net/16/3049/2012/hess-16-3049-2012.html doi:10.5194/hess-16-3049-2012 doi:10.5194/hess-16-3049-2012 |
| spellingShingle | Dawson, C.W Mount, Nick J. Abrahart, R.J Shamseldin, A.Y. Ideal point error for model assessment in data-driven river flow forecasting |
| title | 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_short | 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 |
| url | https://eprints.nottingham.ac.uk/28051/ https://eprints.nottingham.ac.uk/28051/ https://eprints.nottingham.ac.uk/28051/ |