Loughborough University
Leicestershire, UK
LE11 3TU
+44 (0)1509 263171
Loughborough University

Loughborough University Institutional Repository

Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/13000

Title: The need for operational reasoning in data-driven rating curve prediction of suspended sediment
Authors: Mount, Nick J.
Abrahart, Robert J.
Dawson, Christian W.
Ab Ghani, Ngahzaifa
Keywords: Suspended sediment
Data-driven
Rating curve
Modelling
Operational validity
Issue Date: 2012
Publisher: © Wiley-Blackwell
Citation: MOUNT, N.J. ... et al, 2012. The need for operational reasoning in data-driven rating curve prediction of suspended sediment. Hyrdrological Processes, 26 (26), pp.3982-4000.
Abstract: The use of data-driven modelling techniques to deliver improved suspended sediment rating curves has received considerable interest in recent years. Studies indicate an increased level of performance over traditional approaches when such techniques are adopted. However, closer scrutiny reveals that, unlike their traditional counterparts, data-driven solutions commonly include lagged sediment data as model inputs, and this seriously limits their operational application. In this paper, we argue the need for a greater degree of operational reasoning underpinning data-driven rating curve solutions and demonstrate how incorrect conclusions about the performance of a data-driven modelling technique can be reached when the model solution is based upon operationally invalid input combinations. We exemplify the problem through the re-analysis and augmentation of a recent and typical published study, which uses gene expression programming to model the rating curve. We compare and contrast the previously published, solutions, whose inputs negate their operational application, with a range of newly developed and directly comparable traditional and data-driven solutions, which do have operational value. Results clearly demonstrate that the performance benefits of the published gene expression programming solutions are dependent on the inclusion of operationally limiting, lagged data inputs. Indeed, when operationally inapplicable input combinations are discounted from the models and the analysis is repeated, gene expression programming fails to perform, as well as many simpler, more standard multiple linear regression, piecewise linear regression and neural network counterparts. The potential for overstatement of the benefits of the data-driven paradigm in rating curve studies is thus highlighted.
Description: This article is closed access.
Version: Closed access
DOI: 10.1002/hyp.8439
URI: https://dspace.lboro.ac.uk/2134/13000
Publisher Link: http://dx.doi.org/10.1002/hyp.8439
ISSN: 1099-1085
Appears in Collections:Closed Access (Computer Science)

Files associated with this item:

File Description SizeFormat
HYP8439.pdfAccepted version1.45 MBAdobe PDFView/Open

 

SFX Query

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.