2006
DOI: 10.1016/j.ress.2005.03.011
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Application of Bayesian network to the probabilistic risk assessment of nuclear waste disposal

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Cited by 92 publications
(35 citation statements)
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“…BNs have been extensively used in different areas of research such as in the chemical mode of action classification for aquatic toxicology (Carriger et al, 2016) and ecological risk assessment (Lee & Lee, 2006;Pollino et al, 2007); to classify images in medical image analysis (Arias et al, 2016); to predict food fraud ; to detect surgical site infections and safety assessment of natural gas stations (Sohn et al, 2016;Zarei et al, 2016).…”
Section: Introductionmentioning
confidence: 99%
“…BNs have been extensively used in different areas of research such as in the chemical mode of action classification for aquatic toxicology (Carriger et al, 2016) and ecological risk assessment (Lee & Lee, 2006;Pollino et al, 2007); to classify images in medical image analysis (Arias et al, 2016); to predict food fraud ; to detect surgical site infections and safety assessment of natural gas stations (Sohn et al, 2016;Zarei et al, 2016).…”
Section: Introductionmentioning
confidence: 99%
“…BNs, also called causal or probabilistic networks, are largely developed by the artificial intelligence community and they have been applied in a number of diverse problem domains including ecological modelling (Uusitalo, 2007), medical diagnosis (Wiegerinck et al, 1999), image classification (Malka and Lerner, 2004) and fraud detection (Kirkos et al, 2007). A number of publications have used BNs as a method of addressing risk assessment, such as in nuclear waste disposal (Lee and Lee, 2006), neural tube defects (Liao et al, 2010) and in seismic risk (Bayraktarli et al, 2006). Recently BNs have started to make their way into the CCS community for analysing safety risk related to loss of containment in CO 2 transport (Kvien et al, 2013), for combining evidence from multiple CO 2 leak detection technologies in geological storage (Yang et al, 2012) and for discriminating between natural, triggered and induced earthquakes in areas that have geo-engineering operations (Dahm et al, 2010).…”
Section: Bayesian Networkmentioning
confidence: 99%
“…A Bayesian network is a system for the representation of knowledge and the calculation of conditional probabilities, which can be applied to many complex problems [5], [6] and [7].…”
Section: ) P(b)mentioning
confidence: 99%