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...
| Main Authors: | , , , , |
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| Format: | Article |
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Elsevier
2011
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| Online Access: | https://eprints.nottingham.ac.uk/28058/ |
| _version_ | 1848793497566445568 |
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| author | Abrahart, R.J. Mount, Nick J. Ab Ghani, Ngahzaifa Clifford, Nicholas J. Dawson, C.W. |
| author_facet | Abrahart, R.J. Mount, Nick J. Ab Ghani, Ngahzaifa Clifford, Nicholas J. Dawson, C.W. |
| author_sort | Abrahart, R.J. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| 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 hydrological 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 hydrological context of the catchment being modelled. DAMP: a four-point protocol for evaluating the hydrological 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 hydrological 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. |
| first_indexed | 2025-11-14T19:01:14Z |
| format | Article |
| id | nottingham-28058 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:01:14Z |
| publishDate | 2011 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-280582020-05-04T20:23:01Z https://eprints.nottingham.ac.uk/28058/ DAMP: a protocol for contextualising goodness-of-fit statistics in sediment-discharge data-driven modelling Abrahart, R.J. Mount, Nick J. Ab Ghani, Ngahzaifa Clifford, Nicholas J. Dawson, C.W. 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 hydrological 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 hydrological context of the catchment being modelled. DAMP: a four-point protocol for evaluating the hydrological 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 hydrological 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 2011-11 Article PeerReviewed Abrahart, R.J., Mount, Nick J., Ab Ghani, Ngahzaifa, Clifford, Nicholas J. and Dawson, C.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 http://www.sciencedirect.com/science/article/pii/S002216941100610X doi:10.1016/j.jhydrol.2011.08.054 doi:10.1016/j.jhydrol.2011.08.054 |
| spellingShingle | Abrahart, R.J. Mount, Nick J. Ab Ghani, Ngahzaifa Clifford, Nicholas J. Dawson, C.W. DAMP: a protocol for contextualising goodness-of-fit statistics in sediment-discharge data-driven modelling |
| title | 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_short | 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 |
| url | https://eprints.nottingham.ac.uk/28058/ https://eprints.nottingham.ac.uk/28058/ https://eprints.nottingham.ac.uk/28058/ |