DAMP: A protocol for contextualising goodness-of-fit statistics in sediment-discharge data-driven modelling

The decision sequence which guides the selection of a preferred data-driven modelling solution is usually based solely on statistical assessment of fit to a test dataset, and lacks the incorporation of essential contextual knowledge and understanding included in the evaluation of conventional empiri...

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Main Authors:	Abrahart, Robert J., Mount, Nick J., Ngahzaifa, Ab. Ghani, Clifford, Nicholas J., Dawson, Christian W.
Format:	Article
Language:	English
Published:	Elsevier B.V. 2011
Subjects:	TA Engineering (General). Civil engineering (General)
Online Access:	http://umpir.ump.edu.my/id/eprint/25742/ http://umpir.ump.edu.my/id/eprint/25742/ http://umpir.ump.edu.my/id/eprint/25742/ http://umpir.ump.edu.my/id/eprint/25742/1/DAMP-%20A%20protocol%20for%20contextualising%20goodness-of-fit%20statistics%20in%20sediment-discharge.pdf

id	ump-25742
recordtype	eprints
spelling	ump-257422020-02-24T02:14:08Z http://umpir.ump.edu.my/id/eprint/25742/ DAMP: A protocol for contextualising goodness-of-fit statistics in sediment-discharge data-driven modelling Abrahart, Robert J. Mount, Nick J. Ngahzaifa, Ab. Ghani Clifford, Nicholas J. Dawson, Christian W. TA Engineering (General). Civil engineering (General) The decision sequence which guides the selection of a preferred data-driven modelling solution is usually based solely on statistical assessment of fit to a test dataset, and lacks the incorporation of essential contextual knowledge and understanding included in the evaluation of conventional empirical models. This paper demonstrates how hydrologic insight and knowledge of data quality issues can be better incorporated into the sediment-discharge data-driven model assessment procedure: by the plotting of datasets and modelled relationships; and from an understanding and appreciation of the hydrologic context of the catchment being modelled. DAMP: a four-point protocol for evaluating the hydrologic soundness of data-driven single-input single-output sediment rating curve solutions is presented. The approach is adopted and exemplified in an evaluation of seven explicit sediment-discharge models that are used to predict daily suspended sediment concentration values for a small tropical catchment on the island of Puerto Rico. Four neurocomputing counterparts are compared and contrasted against a set of traditional log–log linear sediment rating curve solutions and a simple linear regression model. The statistical assessment procedure provides one indication of the best model, whilst graphical and hydrologic interpretation of the depicted datasets and models challenge this overly-simplistic interpretation. Traditional log–log sediment rating curves, in terms of soundness and robustness, are found to deliver a superior overall product – irrespective of their poorer global goodness-of-fit statistics. Elsevier B.V. 2011 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/25742/1/DAMP-%20A%20protocol%20for%20contextualising%20goodness-of-fit%20statistics%20in%20sediment-discharge.pdf Abrahart, Robert J. and Mount, Nick J. and Ngahzaifa, Ab. Ghani and Clifford, Nicholas J. and Dawson, Christian W. (2011) DAMP: A protocol for contextualising goodness-of-fit statistics in sediment-discharge data-driven modelling. Journal of Hydrology, 409 (3-4). pp. 596-611. ISSN 0022-1694 https://doi.org/10.1016/j.jhydrol.2011.08.054 https://doi.org/10.1016/j.jhydrol.2011.08.054
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TA Engineering (General). Civil engineering (General)
spellingShingle	TA Engineering (General). Civil engineering (General) Abrahart, Robert J. Mount, Nick J. Ngahzaifa, Ab. Ghani Clifford, Nicholas J. Dawson, Christian W. DAMP: A protocol for contextualising goodness-of-fit statistics in sediment-discharge data-driven modelling
description	The decision sequence which guides the selection of a preferred data-driven modelling solution is usually based solely on statistical assessment of fit to a test dataset, and lacks the incorporation of essential contextual knowledge and understanding included in the evaluation of conventional empirical models. This paper demonstrates how hydrologic insight and knowledge of data quality issues can be better incorporated into the sediment-discharge data-driven model assessment procedure: by the plotting of datasets and modelled relationships; and from an understanding and appreciation of the hydrologic context of the catchment being modelled. DAMP: a four-point protocol for evaluating the hydrologic soundness of data-driven single-input single-output sediment rating curve solutions is presented. The approach is adopted and exemplified in an evaluation of seven explicit sediment-discharge models that are used to predict daily suspended sediment concentration values for a small tropical catchment on the island of Puerto Rico. Four neurocomputing counterparts are compared and contrasted against a set of traditional log–log linear sediment rating curve solutions and a simple linear regression model. The statistical assessment procedure provides one indication of the best model, whilst graphical and hydrologic interpretation of the depicted datasets and models challenge this overly-simplistic interpretation. Traditional log–log sediment rating curves, in terms of soundness and robustness, are found to deliver a superior overall product – irrespective of their poorer global goodness-of-fit statistics.
format	Article
author	Abrahart, Robert J. Mount, Nick J. Ngahzaifa, Ab. Ghani Clifford, Nicholas J. Dawson, Christian W.
author_facet	Abrahart, Robert J. Mount, Nick J. Ngahzaifa, Ab. Ghani Clifford, Nicholas J. Dawson, Christian W.
author_sort	Abrahart, Robert J.
title	DAMP: A protocol for contextualising goodness-of-fit statistics in sediment-discharge data-driven modelling
title_short	DAMP: A protocol for contextualising goodness-of-fit statistics in sediment-discharge data-driven modelling
title_full	DAMP: A protocol for contextualising goodness-of-fit statistics in sediment-discharge data-driven modelling
title_fullStr	DAMP: A protocol for contextualising goodness-of-fit statistics in sediment-discharge data-driven modelling
title_full_unstemmed	DAMP: A protocol for contextualising goodness-of-fit statistics in sediment-discharge data-driven modelling
title_sort	damp: a protocol for contextualising goodness-of-fit statistics in sediment-discharge data-driven modelling
publisher	Elsevier B.V.
publishDate	2011
url	http://umpir.ump.edu.my/id/eprint/25742/ http://umpir.ump.edu.my/id/eprint/25742/ http://umpir.ump.edu.my/id/eprint/25742/ http://umpir.ump.edu.my/id/eprint/25742/1/DAMP-%20A%20protocol%20for%20contextualising%20goodness-of-fit%20statistics%20in%20sediment-discharge.pdf
first_indexed	2023-09-18T22:39:43Z
last_indexed	2023-09-18T22:39:43Z
_version_	1777416832944177152

DAMP: A protocol for contextualising goodness-of-fit statistics in sediment-discharge data-driven modelling

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