Scenario Modeling for Radionuclide Soil-To-Plant Transfer Using Dynamic Bayesian Network
The study is aimed at estimating and forecasting the transfer coefficient of radionuclides from soil to agricultural plants based on the real data collected in the areas affected by the Chornobyl disaster. The model was developed using a dynamic Bayesian network, which is an element of novelty, since the use of this tool for radio-ecological modelling was not previously carried out. The factors affecting the radionuclide transfer coefficient were analyzed, and the dependencies transfer level change were identified, depending on the humidity, acidity, soil type, depth of the root system, the content of K+ and Ca2+. Junction tree algorithm was used for inference, as the network consists both of continuous and discrete nodes. The results obtained demonstrate the high accuracy in accordance with general criteria that justifies the use of dynamic Bayesian network to solve this problem. Also the possibility of this approach usage while solving problems of the same class in general was considered. The model allows creating long-term scenarios to identify the possible ways of agriculture development over the areas affected by the Chornobyl disaster and similar anthropogenic disasters.
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