title: A Bayesian regression approach to assess uncertainty in pollutant wash-off modelling.
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.
authors.affiliation: Science and Engineering Faculty, Queensland University of Technology, GPO Box 2434, Brisbane 4001, Australia.
authors.forename: Prasanna
authors.initials: P
authors.lastname: Egodawatta
authors.affiliation: School of Computing, Engineering and Mathematics, University of Western Sydney, Building XB, Locked Bag 1797, Penrith, NSW 2751, Australia.
authors.forename: Khaled
authors.initials: K
authors.lastname: Haddad
authors.affiliation: School of Computing, Engineering and Mathematics, University of Western Sydney, Building XB, Locked Bag 1797, Penrith, NSW 2751, Australia.
authors.forename: Ataur
authors.initials: A
authors.lastname: Rahman
authors.affiliation: Science and Engineering Faculty, Queensland University of Technology, GPO Box 2434, Brisbane 4001, Australia. Electronic address: a.goonetilleke@qut.edu.au.
authors.forename: Ashantha
authors.initials: A
authors.lastname: Goonetilleke
mesh_terms: D001499:Bayes Theorem; D008956:Models, Chemical; D015233:Models, Statistical; D011891:Rain; D035501:Uncertainty; D014872:Water Movements; D014874:Water Pollutants, Chemical / Q000032:analysis; D014876:Water Pollution / Q000706:statistics & numerical data*
chemical_list: D014874:Water Pollutants, Chemical
keywords: Bayesian analysis; Model uncertainty; Monte Carlo simulation; Pollutant wash-off; Stormwater pollutant processes; Stormwater quality