+44 (0)1509 263171
Please use this identifier to cite or link to this item:
|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.|
|Publisher Link: ||http://dx.doi.org/10.5194/hess-16-3049-2012|
|Appears in Collections:||Published Articles (Computer Science)|
Files associated with this item:
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.