Assessing uncertainty in pollutant wash-off modelling via model validation

Haddad, Khaled, Egodawatta, Prasanna, Rahman, Ataur, & Goonetilleke, Ashantha (2014) Assessing uncertainty in pollutant wash-off modelling via model validation. Science of the Total Environment, 497-498, pp. 578-584.

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Stormwater pollution is linked to stream ecosystem degradation. In predicting stormwater pollution, various types of modelling techniques are adopted. The accuracy of predictions provided by these models depends on the data quality, appropriate estimation of model parameters, and the validation undertaken. It is well understood that available water quality datasets in urban areas span only relatively short time scales unlike water quantity data, which limits the applicability of the developed models in engineering and ecological assessment of urban waterways. This paper presents the application of leave-one-out (LOO) and Monte Carlo cross validation (MCCV) procedures in a Monte Carlo framework for the validation and estimation of uncertainty associated with pollutant wash-off when models are developed using a limited dataset. It was found that the application of MCCV is likely to result in a more realistic measure of model coefficients than LOO. Most importantly, MCCV and LOO were found to be effective in model validation when dealing with a small sample size which hinders detailed model validation and can undermine the effectiveness of stormwater quality management strategies.

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3 citations in Scopus
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2 citations in Web of Science®

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ID Code: 75912
Item Type: Journal Article
Refereed: Yes
Additional URLs:
Keywords: model uncertainty, Monte Carlo cross validation, pollutant wash-off, stormwater pollutant processes, stormwater quality
DOI: 10.1016/j.scitotenv.2014.08.027
ISSN: 0048-9697
Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > CIVIL ENGINEERING (090500) > Water Quality Engineering (090508)
Divisions: Current > Schools > School of Earth, Environmental & Biological Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 Elsevier B.V.
Copyright Statement: NOTICE: this is the author’s version of a work that was accepted for publication in Science of the Total Environment. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Science of the Total Environment, [Volumes 497–498, (1 November 2014)] DOI: 10.1016/j.scitotenv.2014.08.027
Deposited On: 03 Sep 2014 23:10
Last Modified: 17 Nov 2016 01:58

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