Data assimilation approaches in the EURANOS project

Kaiser, Jan Christian; Gering, F.; Astrup, Poul; Mikkelsen, Torben

doi:10.1051/radiopro/2010020

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…Each method has its advantages and drawbacks. The current status and practices are described elsewhere in this issue (see the paper by Kaiser et al, 2010).…”

Section: Data Assimilation In An Emergencymentioning

confidence: 99%

Guidance on monitoring and data assimilation

et al. 2010

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Decision makers must react in a prompt and appropriate manner in various emergency situations. The bases for decisions are often predictions produced with decision support systems (DSS). Actual radiation measurement data can be used to improve the reliability of the predictions. Data assimilation is an important link between model calculations and measurements and thus decreases the overall uncertainty of the DSS predictions. However, different aspects have to be taken into account for the optimal use of the data assimilation technique: different countries may have differing measurement strategies and systems as well as differing calculation models. The scenario and the amount and composition of radionuclides released may vary. In this paper we analyse the situation during and after an accident and draw up a list of recommendations that can help modellers to take into account the measurements that are best suited for data assimilation.

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“…Each method has its advantages and drawbacks. The current status and practices are described elsewhere in this issue (see the paper by Kaiser et al, 2010).…”

Section: Data Assimilation In An Emergencymentioning

confidence: 99%

Guidance on monitoring and data assimilation

et al. 2010

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“…In more recent years some progress has been made in uncertainty handling for nuclear emergency management, for example by including statistical methodologies in components of DSSs that previously were deterministic, as shown in [6]. Also data assimilation using the appropriate statistical methodologies has been included in some analyses, as for the atmospheric dispersion of the radiation [7], with the main aim of reducing uncertainty. However, there still exists no complete formal theory about how this uncertainty should be propagated over the system.…”

Section: Introductionmentioning

confidence: 99%

Using graphical models and multi-attribute utility theory for probabilistic uncertainty handling in large systems, with application to the nuclear emergency management

Leonelli

Smith

2013

2013 IEEE 29th International Conference on Data Engineering Workshops (ICDEW)

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Abstract-Although many decision-making problems involve uncertainty, uncertainty handling within large decision support systems (DSSs) is challenging. One domain where uncertainty handling is critical is emergency response management, in particular nuclear emergency response, where decision making takes place in an uncertain, dynamically changing environment. Assimilation and analysis of data can help to reduce these uncertainties, but it is critical to do this in an efficient and defensible way. After briefly introducing the structure of a typical DSS for nuclear emergencies, the paper sets up a theoretical structure that enables a formal Bayesian decision analysis to be performed for environments like this within a DSS architecture. In such probabilistic DSSs many input conditional probability distributions are provided by different sets of experts overseeing different aspects of the emergency. These probabilities are then used by the decision maker (DM) to find her optimal decision. We demonstrate in this paper that unless due care is taken in such a composite framework, coherence and rationality may be compromised in a sense made explicit below. The technology we describe here builds a framework around which Bayesian data updating can be performed in a modular way, ensuring both coherence and efficiency, and provides sufficient unambiguous information to enable the DM to discover her expected utility maximizing policy.

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Data assimilation approaches in the EURANOS project

Cited by 2 publications

References 4 publications

Guidance on monitoring and data assimilation

Guidance on monitoring and data assimilation

Using graphical models and multi-attribute utility theory for probabilistic uncertainty handling in large systems, with application to the nuclear emergency management

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