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

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

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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.

Item Type: Article
Schools/Departments: University of Nottingham, UK > Faculty of Social Sciences > School of Geography
Identification Number:
Depositing User: Mount, Dr Nick
Date Deposited: 30 Jan 2015 14:18
Last Modified: 04 May 2020 20:22

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