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A Bayesian Space-Time Dynamic Linear Model for Radioactivity Deposition after a Nuclear Accident

De, Swarup (2009). A Bayesian Space-Time Dynamic Linear Model for Radioactivity Deposition after a Nuclear Accident. PhD thesis. The Open University.

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Abstract

A three-stage Hierarchical Bayesian Space-Time (HBST) model, an extension of the class of dynamic linear models to space, is proposed for the ground contamination levels (GCL) and related uncertainties caused by polluting discharges in the environment. The model should allow updating the distribution of GCL in time and space as new data in the form of measurements and expert judgements become available to give real-time estimates of deposition levels.

An application of the HBST model is proposed for the statistical modelling of radioactivity deposition after a nuclear accident. It explicitly handles uncertainties associated with (i) predictions of depositions from a long-range atmospheric dispersal model, (ii) in-situ gamma ray measurements and (iii) spatial interpolations. Unlike existing environmental statistical models, the HBST model also accounts for an established food chain contamination model called ECOSYS for which it provides data assimilation capabilities.

The HBST model permits a fast implementation and full probabilistic inference for the parameters, interpolation and forecasts. Three distinct formulations of the HBST model were applied to assimilate real data of radioactivity deposition from the Chernobyl accident in southern Germany. Two of those formulations differ on the functional form of their spatial covariance matrices while the third, a normal inverse-Wishart model, allows the spatial covariances to "learn" from the data within the usual Bayesian paradigm. The later is shown to outperform the former models both in short and medium term forecasting as well as in a predictive interpolation test that took some measurements as out-of-sample.

Item Type: Thesis (PhD)
Copyright Holders: 2009 The Author
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
Item ID: 60198
Depositing User: ORO Import
Date Deposited: 28 Mar 2019 15:56
Last Modified: 12 Jun 2020 04:12
URI: http://oro.open.ac.uk/id/eprint/60198
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