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Title: Ideal point error for model assessment in data-driven river flow forecasting
Authors: Dawson, Christian W.
Mount, Nick J.
Abrahart, Robert J.
Shamseldin, Asaad Y.
Issue Date: 2012
Publisher: Published by Copernicus Publications on behalf of the European Geosciences Union. (© Author(s))
Citation: DAWSON, C.W. ... et al, 2012. Ideal point error for model assessment in data-driven river flow forecasting. Hydrology and Earth System Sciences, 16 (8), pp.3049-3060.
Abstract: 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.
Description: This work is distributed under the Creative Commons Attribution 3.0 License.
Version: Published
DOI: 10.5194/hess-16-3049-2012
URI: https://dspace.lboro.ac.uk/2134/13004
Publisher Link: http://dx.doi.org/10.5194/hess-16-3049-2012
ISSN: 1027-5606
Appears in Collections:Published Articles (Computer Science)

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