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The need for operational reasoning in data-driven rating curve prediction of suspended sediment
journal contribution
posted on 2013-08-22, 13:33 authored by Nick J. Mount, Robert J. Abrahart, Christian DawsonChristian Dawson, Ngahzaifa Ab GhaniThe 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.
History
School
- Science
Department
- Computer Science
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.Publisher
© Wiley-BlackwellVersion
- NA (Not Applicable or Unknown)
Publication date
2012Notes
This article is closed access.ISSN
1099-1085Publisher version
Language
- en