The paper discusses the problems associated with environmental modelling and the need to consider uncertainty in the formulation, identification, estimation and validation of environmental models. It introduces the concept of Data-Based Mechanistic (DBM) modelling and contrasts its inductive approach with the hypotheticodeductive approaches that dominate most environmental modelling research at...

BACKGROUND Spatial modelling of STH and schistosomiasis epidemiology is now commonplace. Spatial epidemiological studies help inform decisions regarding the number of people at risk as well as the geographic areas that need to be targeted with mass drug administration; however, limited attention has been given to propagated uncertainties, their interpretation, and consequences for the mapped va...

Computer scientists are often faced with the challenge of having to model the world and its associated uncertainties. One area in particular where modelling uncertainty is important are Expert Systems (also referred to as Knowledge Based Systems and Intelligent Systems), where procedural / classification knowledge is often captured as facts and rules. One of the earliest Expert Systems to incor...

The uncertainty of water quality predictions caused by uncertainty in the inputs related to emissions of diffuse pollution is analysed. An uncertainty analysis of the effects of diffuse pollution is essential to compare the cost and benefits of measures to lower those emissions. We focus on diffuse nitrate pollution due to fertiliser use. Using an efficient Monte Carlo method and Latin Hypercub...

Process-based models of nutrient transport are often used as tools for management of eutrophic waters, as decision makers need to judge the potential effects of alternative remediation measures, under current conditions and with future land use and climate change. All modelling exercises entail uncertainty arising from various sources, such as the input data, selection of parameter values and t...

How can spatially explicit nonlinear regression modelling be used for obtaining nonpoint source loading estimates in watersheds with limited information? What is the value of additional monitoring and where should future data-collection efforts focus on? In this study, we address two frequently asked questions in watershed modelling by implementing Bayesian inference techniques to parameterize ...

The parametric uncertainty may occur in modelling and description of real systems as a consequence of inaccurate measurement, identification, due to ambient conditions, etc. The structure of systems with parametric uncertainty is known but parameters can vary within given or supposed intervals. The fundamental factor for choosing of appropriate robust stability analysis tool is the uncertainty ...

Models and modelling methods play an essential role in Operational Research and Management Science (OR/MS). This article presents four models which concern how OR/MS employs different modelling methods for different modelling tasks, under different constraints, and for different forms of uncertainty. Two of these “metamodels” concern how OR/MS modelling has been employed in decision support for...

We briefly outline the need to incorporate uncertainty and flexibility into the semantic web knowledge representation, and argue that support logic programming a combination of soft computing and logic programming – is one way to address this problem. To retain consistency with emerging frameworks, we show how a support logic program modelling uncertainty in relations and rules can be compiled ...