Uncertainty analysis of pollutant build-up modelling based on a Bayesian Weighted Least Squares approach

Khaled, Haddad, Egodawatta, Prasanna, Rahman, Ataur, & Goonetilleke, Ashantha (2013) Uncertainty analysis of pollutant build-up modelling based on a Bayesian Weighted Least Squares approach. Science of the Total Environment, 449, pp. 410-417.

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Abstract

Reliable pollutant build-up prediction plays a critical role in the accuracy of urban stormwater quality modelling outcomes. However, water quality data collection is resource demanding compared to streamflow data monitoring, where a greater quantity of data is generally available. Consequently, available water quality data sets span only relatively short time scales unlike water quantity data. Therefore, the ability to take due consideration of the variability associated with pollutant processes and natural phenomena is constrained. This in turn gives rise to uncertainty in the modelling outcomes as research has shown that pollutant loadings on catchment surfaces and rainfall within an area can vary considerably over space and time scales. Therefore, the assessment of model uncertainty is an essential element of informed decision making in urban stormwater management. This paper presents the application of a range of regression approaches such as ordinary least squares regression, weighted least squares Regression and Bayesian Weighted Least Squares Regression for the estimation of uncertainty associated with pollutant build-up prediction using limited data sets. The study outcomes confirmed that the use of ordinary least squares regression with fixed model inputs and limited observational data may not provide realistic estimates. The stochastic nature of the dependent and independent variables need to be taken into consideration in pollutant build-up prediction. It was found that the use of the Bayesian approach along with the Monte Carlo simulation technique provides a powerful tool, which attempts to make the best use of the available knowledge in the prediction and thereby presents a practical solution to counteract the limitations which are otherwise imposed on water quality modelling.

Impact and interest:

15 citations in Scopus
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13 citations in Web of Science®

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ID Code: 57726
Item Type: Journal Article
Refereed: Yes
Keywords: uncertainty analysis, Bayesian analysis, Monte Carlo simulation, stormwater quality, pollutant build-up, stormwater pollutant processes
DOI: 10.1016/j.scitotenv.2013.01.086
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 2013 Elsevier
Copyright Statement: This is the author’s version of a work that was accepted for publication in the journal, 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, Volume 449, (2013), DOI: 10.1016/j.scitotenv.2013.01.086
Deposited On: 04 Mar 2013 01:38
Last Modified: 08 Aug 2014 04:31

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