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Webové prostředí pro atmosférické disperzní modelování
Hofman, Radek ; Pecha, Petr
V případě úniků radionuklidů do ovzduší se pro modelování jejich šíření používají komplexní atmosférické disperzní modely. Doménou těchto sys- tému jsou desktopové aplikace, mnohdy provozované na superpočítačích. Existují ovšem i výjimky, jako například americký systém IXP (International Exchange Program - http://ixp.llnl.gov, [3]), který umožňuje provádět simu- lace disperze vzdušného znečistění ve webovém prostředí. Jednou z hlavních výhod webového prostředí je fakt, že odpadá nutnost jakékoliv instalace software ze strany uživatele, který po přihlášení do systému pracuje s nejno- vější verzí modelu. Použití se neomezuje pouze na klasická PC, ale aplikaci je možno používat i v prohlížečích mobilních zařízení. Naším cílem je vybudovat obdobnou službu.
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Bayesian Methods for Optimization of Radiation Monitoring Networks
Šmídl, Václav ; Hofman, Radek
Release of radioactive material into the atmosphere is the last possible resort of any accident in a nuclear power plant. It is an extremely rare event, however with severe consequences for potentially many people living in proximity of the power plant. Awareness of radiation security has been increased after the Chernobyl accident, and almost every country is now equipped with monitoring network of on-line connected receptors continually measuring radiation levels. Initial configurations of the network were designed by experts using their experience.In this report, we are concerned with local scale modeling of less severe accident in the range of tens of kilometers from the nuclear power plant. Both the stationary and mobile groups will be discussed. The preferred model of uncertainty is the empirical density which will be assimilated with measurements using the sequential Monte Carlo methodology. We will discuss influence of various loss functions.
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Application of Sequential Monte Carlo Estimation for Early Phase of Radiation Accident
Šmídl, Václav ; Hofman, Radek
The early phase of radiation accident is characterized by minimum number of measured data and high uncertainty in both atmospheric conditions and radiation situation. Our goal is to provide an accurate method of radiation situation assessment that is capable to respect the uncertainty and provide informative predictions of its evolution for the involved decision makers. We propose a state space model based on atmospheric dispersion model, numerical weather model with local corrections and random walk on the model corrections and release evolution. This model is highly nonlinear and is estimated using sequential Monte Carlo. Since the model is significantly more complex that previously considered models and its estimation with naive proposal densities become too computationally demanding. We propose to construct a proposal density using problem specific simplification followed by application of the Laplace approximation. Properties of the resulting estimation procedure are illustrated on a twin experiment.
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Data assimilation methods for early and late phase of radiation accident - A comprehensive review with examples of methods examined within solution of the grant projet VG20102013018
Hofman, Radek ; Pecha, Petr ; Šmídl, Václav
The task of the decision support in the case of a radiation accident is to provide up-to-date information on the radiation situation, prognosis of its future evolution and possible conse- quences. The reliability of predictions can be significantly improved using data assimilation, which refers to a group of mathematical methods allowing an efficient combination of ob- served data with a numerical model. The concerns application of the advanced data assimilation methods in the field of radiation protection. We focus on assessment of off-site consequences in the case of a radiation accident when radionuclides are released into the environment. In this report we present a comprehensive review of data assimilation evaluated for pur- poses of inclusion into the data assimilation and decision support system ASIM developed within the grant project VG20102013018 provided by the Ministry of the Interior of the Czech Republic.
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Modelling of Traffic Flow with Bayesian Autoregressive Model with Variable Partial Forgetting
Dedecius, Kamil ; Nagy, Ivan ; Hofman, Radek
Computing the future road traffic intensities in urban and suburban areas is considered inthis paper. The statistical properties of the traffic flow advocate the use of a low-order lin- ear autoregressive models, in which the previous intensities determine the following ones. To achieve adaptivity, the Bayesian modelling framework was chosen. The regression coefficients are considered random, hence they are modelled using a suitable distribution. A significant improvement of the overall modelling performance is further reached with techniques allowing the parameters vary by modification of their distribution. We present the partial forgetting method, allowing to individually track the parameters even in the case of their different variability rate.
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