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.

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.

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), https://doi.org/10.1002/hyp.8439

• Journal Article Type: Article
• Publication Date: Feb 21, 2012
• Deposit Date: Jan 30, 2015
• Publicly Available Date: Jan 30, 2015
• Journal: Hydrological Processes
• Print ISSN: 0885-6087
• Electronic ISSN: 1099-1085
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• Volume: 26
• Issue: 26
• DOI: https://doi.org/10.1002/hyp.8439
• Keywords: suspended sediment; data-driven; rating curve; modelling; operational validity
• Public URL: https://nottingham-repository.worktribe.com/output/709345
• Publisher URL: http://onlinelibrary.wiley.com/doi/10.1002/hyp.8439/abstract
• 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.wiley.com/doi/10.1002/hyp.8439/abstract