Predicting future probable values of model parameters, is an essential pre-requisite for assessing model decision reliability in an uncertain environment. Scenario Analysis is a methodology for modelling uncertainty in water resources management modelling. Uncertainty if not considered appropriately in decision making will decrease reliability of decisions, especially in long-term planning. One...

Source term estimation (STE) of hazardous material (HAZMAT) releases is critical for emergency response. Such problem is usually solved with the aid of atmospheric dispersion modelling and inversion algorithms accompanied with a variety of uncertainty, including uncertainty in atmospheric dispersion models, uncertainty in meteorological data, uncertainty in measurement process and uncertainty i...

Uncertainty is an intrinsic feature of complex ecological models. Given that it is not possible to rid the models from uncertainty, we are left with taking notice of it for consideration in model-based decision making. Traditional ecological modelling methods and tools do not support explicit accounts of model uncertainty. This work gives a contribution towards making known, or bringing to the ...

This paper develops a new simulation-based measure of playoff uncertainty and investigates its contribution to modelling match attendance compared to other variants of playoff uncertainty in the existing literature. A model of match attendance that incorporates match uncertainty, playoff uncertainty, past home-team performance and other relevant control variables is fitted to Australian Nationa...

Nowadays, the use of freeform surfaces in various functional applications has become more widespread. Multi-sensor coordinate measuring machines (CMMs) are becoming popular and are produced by many CMM manufacturers since their measurement ability can be significantly improved with the help of different kinds of sensors. Moreover, the measurement accuracy after data fusion for multiple sensors ...

This thesis focuses on computer modelling issues such as i) uncertainty, including uncertainty in parameters, data input and model structure, ii) model complexity and how it affects uncertainty, iii) scale, as it pertains to scaling calibrated and validated models up or down to different spatial and temporal resolutions, and iv) transferability of a model to a site of the same scale. The discus...

This article gives a tutorial introduction on label semantics framework on reasoning with uncertainty. Modelling real world problems typically involves processing uncertainty of two distinct types. These are uncertainty arising from a lack of knowledge relating to concepts which, in the sense of classical logic, may be well defined and uncertainty due to inherent vagueness in concepts themselve...

Uncertainty analysis for model simulations is assuming a growing importance in the field of water quality management. The importance of this concern is provided by recent public awareness over health risks from improper disposal of toxic wastes as well as by the continuing emphasis on risk assessment. The first step in the chain of the risk assessment is the quantification of the error in predi...

In this paper we present work on the modelling of uncertainty by means of prosodic cues in an articulatory speech synthesizer. Our stimuli are embedded into short dialogues in question-answering situations in a human-machine scenario. The answers of the robot vary with respect to the intended level of (un)certainty, the independent variables are intonation (rising vs. falling) and filler (absen...