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...

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Main Authors:	Dawson, C.W, Mount, Nick J., Abrahart, R.J, Shamseldin, A.Y.
Format:	Article
Language:	English
Published:	European Geosciences Union (EGU) 2012
Online Access:	http://eprints.nottingham.ac.uk/28051/ http://eprints.nottingham.ac.uk/28051/ http://eprints.nottingham.ac.uk/28051/ http://eprints.nottingham.ac.uk/28051/1/hess-16-3049-2012.pdf

id	nottingham-28051
recordtype	eprints
spelling	nottingham-280512017-10-13T18:10:12Z http://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 application/pdf en cc_by http://eprints.nottingham.ac.uk/28051/1/hess-16-3049-2012.pdf Dawson, C.W and Mount, Nick J. and 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
repository_type	Digital Repository
institution_category	Local University
institution	University of Nottingham Malaysia Campus
building	Nottingham Research Data Repository
collection	Online Access
language	English
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.
format	Article
author	Dawson, C.W Mount, Nick J. Abrahart, R.J Shamseldin, A.Y.
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
author_facet	Dawson, C.W Mount, Nick J. Abrahart, R.J Shamseldin, A.Y.
author_sort	Dawson, C.W
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	European Geosciences Union (EGU)
publishDate	2012
url	http://eprints.nottingham.ac.uk/28051/ http://eprints.nottingham.ac.uk/28051/ http://eprints.nottingham.ac.uk/28051/ http://eprints.nottingham.ac.uk/28051/1/hess-16-3049-2012.pdf
first_indexed	2018-09-06T11:47:28Z
last_indexed	2018-09-06T11:47:28Z
_version_	1610858573031538688

Ideal point error for model assessment in data-driven river flow forecasting

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