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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/25742/1/DAMP-%20A%20protocol%20for%20contextualising%20goodness-of-fit%20statistics%20in%20sediment-discharge.pdf
http://umpir.ump.edu.my/id/eprint/25742/
https://doi.org/10.1016/j.jhydrol.2011.08.054
https://doi.org/10.1016/j.jhydrol.2011.08.054
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spelling my.ump.umpir.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
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
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/25742/1/DAMP-%20A%20protocol%20for%20contextualising%20goodness-of-fit%20statistics%20in%20sediment-discharge.pdf
http://umpir.ump.edu.my/id/eprint/25742/
https://doi.org/10.1016/j.jhydrol.2011.08.054
https://doi.org/10.1016/j.jhydrol.2011.08.054
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score 13.211869

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