2021
DOI: 10.5194/hess-25-193-2021
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Multivariate autoregressive modelling and conditional simulation for temporal uncertainty analysis of an urban water system in Luxembourg

Abstract: Abstract. Uncertainty is often ignored in urban water systems modelling. Commercial software used in engineering practice often ignores the uncertainties of input variables and their propagation because of a lack of user-friendly implementations. This can have serious consequences, such as the wrong dimensioning of urban drainage systems (UDSs) and the inaccurate estimation of pollution released to the environment. This paper introduces an uncertainty analysis in urban drainage modelling, built on existing met… Show more

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Cited by 3 publications
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“…Examples include assess risk of WWTP effluent exceeding regulatory requirements and potential savings in comprehensive plant optimization (Benedetti et al, 2006;Rousseau et al, 2001). Advantages of Monte Carlo include relatively easy to understand, assessment of the uncertainty in model output via sensitivity analysis and identification of major input factor responsible for most of the model output variability (Korving et al, 2002;Sriwastava et al, 2018;Tavakol-Davani et al, 2019;Torres-Matallana et al, 2020). Disadvantages of Monte Carlo include output accuracy depends on utilization of reasonable/fair assumptions, tendency to underestimate risk events, computational requirements, time-consuming and susceptible to overfitting (Dilks et al, 1992;Han et al, 2007;Thorndahl et al, 2008).…”
Section: Modelling-based Studiesmentioning
confidence: 99%
“…Examples include assess risk of WWTP effluent exceeding regulatory requirements and potential savings in comprehensive plant optimization (Benedetti et al, 2006;Rousseau et al, 2001). Advantages of Monte Carlo include relatively easy to understand, assessment of the uncertainty in model output via sensitivity analysis and identification of major input factor responsible for most of the model output variability (Korving et al, 2002;Sriwastava et al, 2018;Tavakol-Davani et al, 2019;Torres-Matallana et al, 2020). Disadvantages of Monte Carlo include output accuracy depends on utilization of reasonable/fair assumptions, tendency to underestimate risk events, computational requirements, time-consuming and susceptible to overfitting (Dilks et al, 1992;Han et al, 2007;Thorndahl et al, 2008).…”
Section: Modelling-based Studiesmentioning
confidence: 99%