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

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,...

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Main Authors:	Mount, Nick J., Abraharta, Robert J., Dawson, Christian W., Ngahzaifa, Ab. Ghani
Format:	Article
Language:	English
Published:	John Wiley & Sons 2012
Subjects:	QA76 Computer software
Online Access:	http://umpir.ump.edu.my/id/eprint/26633/ http://umpir.ump.edu.my/id/eprint/26633/ http://umpir.ump.edu.my/id/eprint/26633/ http://umpir.ump.edu.my/id/eprint/26633/1/The%20need%20for%20operational%20reasoning%20in%20data-driven%20rating%20curve%20prediction.pdf

id	ump-26633
recordtype	eprints
spelling	ump-266332020-02-27T03:13:41Z http://umpir.ump.edu.my/id/eprint/26633/ The need for operational reasoning in data-driven rating curve prediction Mount, Nick J. Abraharta, Robert J. Dawson, Christian W. Ngahzaifa, Ab. Ghani QA76 Computer software 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. John Wiley & Sons 2012 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/26633/1/The%20need%20for%20operational%20reasoning%20in%20data-driven%20rating%20curve%20prediction.pdf Mount, Nick J. and Abraharta, Robert J. and Dawson, Christian W. and Ngahzaifa, Ab. Ghani (2012) The need for operational reasoning in data-driven rating curve prediction. Hydrological Processes, 26 (26). pp. 3982-3400. ISSN 0885-6087 https://doi.org/10.1002/hyp.8439 https://doi.org/10.1002/hyp.8439
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA76 Computer software
spellingShingle	QA76 Computer software Mount, Nick J. Abraharta, Robert J. Dawson, Christian W. Ngahzaifa, Ab. Ghani The need for operational reasoning in data-driven rating curve prediction
description	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.
format	Article
author	Mount, Nick J. Abraharta, Robert J. Dawson, Christian W. Ngahzaifa, Ab. Ghani
author_facet	Mount, Nick J. Abraharta, Robert J. Dawson, Christian W. Ngahzaifa, Ab. Ghani
author_sort	Mount, Nick J.
title	The need for operational reasoning in data-driven rating curve prediction
title_short	The need for operational reasoning in data-driven rating curve prediction
title_full	The need for operational reasoning in data-driven rating curve prediction
title_fullStr	The need for operational reasoning in data-driven rating curve prediction
title_full_unstemmed	The need for operational reasoning in data-driven rating curve prediction
title_sort	need for operational reasoning in data-driven rating curve prediction
publisher	John Wiley & Sons
publishDate	2012
url	http://umpir.ump.edu.my/id/eprint/26633/ http://umpir.ump.edu.my/id/eprint/26633/ http://umpir.ump.edu.my/id/eprint/26633/ http://umpir.ump.edu.my/id/eprint/26633/1/The%20need%20for%20operational%20reasoning%20in%20data-driven%20rating%20curve%20prediction.pdf
first_indexed	2023-09-18T22:41:34Z
last_indexed	2023-09-18T22:41:34Z
_version_	1777416950239985664

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

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