Modeling Uncertainty in Risk Assessment Using Double Monte Carlo Method

The main aim of risk assessment is to determine the potential detriment to human health from exposure to a substance or activity that under plausible circumstances can cause to human health. Risk assessment models involve inputs which may not be precisely known. The uncertainty of the inputs gets propagated to the output risk. So we need to quantify the uncertainty so as to be aware of the risk involved in any decision making process. Uncertainties can be modeled and analyzed using different theories, viz. Probability theory, Possibility theory, Evidence theory etc. Modeling of an uncertain parameter depends on the nature of the information available. In this paper we have considered uncertainty quantification of parameters in the case of radiological risk assessment. We have analyzed the propagation of the risk both in terms of probability and possibility theory. An advanced method of probabilistic risk assessment (PRAs) viz. Double Monte Carlo method is discussed in this paper. A case study is carried out with this method and compared with the results taking the parameters of the input distribution of the model as Fuzzy number. calculation methods. Probability Bounds Analysis (PBA) is related to one of these methods. It is a combination of probability theory and Interval Analysis. Probability Theory is used to propagate Aleatory Uncertainty (or variability) and Interval Analysis is used to propagate Epistemic Uncertainty (or Uncertainty). Probabilistic approaches characterize the uncertainty in the parameter by a probability distribution. If the input variables of the risk assessment model consist of variability and uncertainties, two interpretations are generally proposed for the distribution of the input variable. First, Uncertainty regarding variability may be viewed in terms of probability regarding frequencies. Second variability is described by frequency distributions, and that uncertainty in general, including sampling error, measurement error, and estimates based upon judgment, is described by probability distribution. The most widely used method in PRA is Monte Carlo Analysis (MCA), which is a means of quantifying uncertainty or variability in a probabilistic framework using computer simulation. One of the advanced modeling approaches that may be used to conduct PRA studies is Two-dimensional Monte Carlo analysis (2D MCA). A 2D MCA is a term used to describe a model that simulates both uncertainty and variability in one and more input variables. The uncertainty is characterized by a p-box in the case of the