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|DOI (Digital Object Identifier) Link:||https://doi.org/10.1111/j.1467-9892.2010.00718.x|
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Dynamic spatial Bayesian (DSB) models are proposed for the analytical modelling of radioactivity deposition after a nuclear accident. The proposed models are extensions of the multi-variate time-series dynamic linear models of West and Harrison (1997) to Markov random field processes. They combine the outputs from a long-range atmospheric dispersal model with measured data (and prior information) to provide improved deposition prediction in space and time. Two versions of a Gaussian DSB model were applied to the radioactivity deposition in Bavaria over a 15 days period during the Chernobyl nuclear accident. One version had fixed functional forms for its spatial variances and covariances while the other allowed those to adapt and ‘learn’ from data in the conjugate Bayesian paradigm. There were two main sources of information for radioactivity deposition in our application: radioactivity measurements at a sparse set of 13 monitoring stations, and the numerical deposition evaluation of the atmospheric dispersal K-model for the points of a 64 x 64 regular grid. We have analysed the temporal predictions (one-step-ahead forecasting) of those DSB models to show that the dispersal K-model tended in general to underestimate the deposition levels at all times while the DSB models corrected for that although with different degrees of adjustment.
|Item Type:||Journal Article|
|Copyright Holders:||2011 Blackwell Publishing Ltd.|
|Keywords:||Bayesian space-time model; Chernobyl nuclear accident; environmental statistics; Gaussian Markov random field process; radioactivity deposition.|
|Academic Unit/Department:||Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
Faculty of Science, Technology, Engineering and Mathematics (STEM)
|Depositing User:||Àlvaro Faria|
|Date Deposited:||03 Nov 2011 12:02|
|Last Modified:||08 Oct 2016 01:52|
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