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The need for operational reasoning in data-driven rating curve prediction of suspended sediment

Mount, Nick J.; Abrahart, R.J.; Dawson, C.W.; Ab Ghani, N.

Authors

Nick J. Mount nick.mount@nottingham.ac.uk

R.J. Abrahart bob.abrahart@nottingham.ac.uk

C.W. Dawson c.w.dawson1@lboro.ac.uk

N. Ab Ghani

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.

Journal Article Type Article
Publication Date Feb 21, 2012
Journal Hydrological Processes
Print ISSN 0885-6087
Electronic ISSN 1099-1085
Publisher Wiley
Peer Reviewed Peer Reviewed
Volume 26
Issue 26
Institution Citation Mount, N. J., Abrahart, R., Dawson, C., & Ab Ghani, N. (2012). The need for operational reasoning in data-driven rating curve prediction of suspended sediment. Hydrological Processes, 26(26), doi:10.1002/hyp.8439
DOI https://doi.org/10.1002/hyp.8439
Keywords suspended sediment; data-driven; rating curve; modelling; operational validity
Publisher URL http://onlinelibrary.wiley.com/doi/10.1002/hyp.8439/abstract
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf
Additional Information NOTE This is the accepted version of the following article: Mount, N.J., Abrahart, R.J., Dawson, C.W. and Ab Ghani, N. (2012), The need for operational reasoning in data-driven rating curve prediction of suspended sediment. Hydrological Processes, 26: 3982–4000. doi: 10.1002/hyp.8439, which has been published in final form at http://onlinelibrary.wi....1002/hyp.8439/abstract

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf




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