Urban stormwater quality modelling plays a central role in evaluation of the quality of the receiving water body. However, the complexity of the physical processes that must be simulated and the limited amount of data available for calibration may lead to high uncertainty in the model results. This study was conducted to assess modelling uncertainty associated with catchment surface pollution e...

The physical modelling and simulation of systems with inherent uncertainty still poses significant issues when using Modelica and its tools. At present, both language and tools are fundamentally deterministic and offer limited support for handling uncertainty; this limits the scope of using Modelica in certain domains, e.g. feedback control systems. We propose a framework for incorporating unce...

Numerous types of uncertainty arise when using formal models in the analysis of risks. Uncertainty is best seen as a relation, allowing a clear separation of the object, source and 'owner' of the uncertainty, and we argue that all expressions of uncertainty are constructed from judgements based on possibly inadequate assumptions, and are therefore contingent. We consider a five-level structure ...

Quantitative microbial risk analysis modelling is increasingly being used in food safety as a tool to evaluate health risks. Accurately forming such models can be very difficult due to the uncertainty in the available data. Second order Monte Carlo simulation allows for the inclusion of this uncertainty and separates it from the variability incorporated in the model. This modelling process is i...