Reducing Parameter Uncertainty Using Bayesian Updating Techniques. Igor Linkov, Menzie-Cura and Associates, Inc., One Courthouse Ln., Suite 2, Chelmsford, MA 01824, USA, telephone +1 978 453 4300 (ext 15), fax +1 978 453 7260, e-mail ilinkov@ma.ultranet.com; and Dmitriy Burmistrov, Urals Center for Radiation Medicine, Russia
Our current understanding of fundamental processes that influence contaminant behavior in contaminated ecosystems is very limited. There is: (i) uncertainty in our knowledge, and (ii) natural variability of the ecosystems. Nevertheless, remediation of contaminated areas requires immediate policy decisions. Bayesian updating has been shown to be a useful tool for making decisions. It allows incorporation of the available experimental data to reconcile and reduce uncertainty both in the input parameters of a model and the future model predictions. This paper illustrates an application of the Bayesian updating for radioecology in general and specifically for contaminated forests of the Chernobyl Exclusion Zone and for contaminated Techa River, Russia. The results of uncertainty reduction in a dynamic model FORESTPATH for the fate and transport of radionuclides in the Chernobyl forests and Techa River Model are presented. The implementation of the Bayesian updating is discussed: uncertainty in the input data determines the model complexity and the selection of the Bayesian updating technique.
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