Bayesian regression approach to assess uncertainty

What is it about?

Water quality data is inherently uncertain which can lead to significant uncertainty in modelling outcomes. Therefore, quantifying this uncertainty is very important in stormwater management decision making. This paper proposes an advanced statistical method, namely, Bayesian/Gibbs sampling regression framework to assess model uncertainty. Secondly, the proposed approach is applied to assess the uncertainty associated with pollutant wash-off modelling outcomes.

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Why is it important?

The uncertainty associated with urban water quality modelling can be significant and needs to be consided in decision making based on the modelling outcomes. However, it is very rarely assessed. This paper proposes an advanced statistical method to assess the modelling undertainty. Additionally, the practical application of this method is demonstrated for pollutant wash-off modelling from roofs.