A Bayesian regression approach to assess uncertainty in pollutant wash-off modelling

Egodawatta, Prasanna, Haddad, Khaled, Rahman, Ataur, & Goonetilleke, Ashantha (2014) A Bayesian regression approach to assess uncertainty in pollutant wash-off modelling. Science of the Total Environment, 479-480, pp. 233-240.

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Abstract

Due to knowledge gaps in relation to urban stormwater quality processes, an in-depth understanding of model uncertainty can enhance decision making. Uncertainty in stormwater quality models can originate from a range of sources such as the complexity of urban rainfall-runoff-stormwater pollutant processes and the paucity of observed data. Unfortunately, studies relating to epistemic uncertainty, which arises from the simplification of reality are limited and often deemed mostly unquantifiable. This paper presents a statistical modelling framework for ascertaining epistemic uncertainty associated with pollutant wash-off under a regression modelling paradigm using Ordinary Least Squares Regression (OLSR) and Weighted Least Squares Regression (WLSR) methods with a Bayesian/Gibbs sampling statistical approach. The study results confirmed that WLSR assuming probability distributed data provides more realistic uncertainty estimates of the observed and predicted wash-off values compared to OLSR modelling. It was also noted that the Bayesian/Gibbs sampling approach is superior compared to the most commonly adopted classical statistical and deterministic approaches commonly used in water quality modelling. The study outcomes confirmed that the predication error associated with wash-off replication is relatively higher due to limited data availability. The uncertainty analysis also highlighted the variability of the wash-off modelling coefficient k as a function of complex physical processes, which is primarily influenced by surface characteristics and rainfall intensity.

Impact and interest:

6 citations in Scopus
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5 citations in Web of Science®

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ID Code: 68173
Item Type: Journal Article
Refereed: Yes
Additional URLs:
Keywords: model uncertainty, stormwater quality, pollutant wash-off, Bayesian analysis, Monte Carlo simulation, stormwater pollutant processes
DOI: 10.1016/j.scitotenv.2014.02.012
ISSN: 0048-9697
Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > CIVIL ENGINEERING (090500)
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 479–480, (1 May 2014)] DOI: 10.1016/j.scitotenv.2014.02.012
Deposited On: 09 Mar 2014 23:16
Last Modified: 03 May 2016 03:08

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