Adaptive Bayesian Algorithms for the ...
Type de document :
Communication dans un congrès avec actes
Titre :
Adaptive Bayesian Algorithms for the Estimation of Source Term in a Complex Atmospheric Release
Auteur(s) :
Ickowicz, Adrien [Auteur]
LAGIS-SI
Septier, Francois [Auteur]
LAGIS-SI
Armand, Patrick [Auteur]
DAM Île-de-France [DAM/DIF]
Delignon, Yves [Auteur]
LAGIS-SI
LAGIS-SI
Septier, Francois [Auteur]
LAGIS-SI
Armand, Patrick [Auteur]
DAM Île-de-France [DAM/DIF]
Delignon, Yves [Auteur]
LAGIS-SI
Titre de la manifestation scientifique :
15th International Conference on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes
Ville :
Madrid
Pays :
Espagne
Date de début de la manifestation scientifique :
2013-05-06
Titre de l’ouvrage :
15th International Conference on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes
Date de publication :
2013-05-06
Mot(s)-clé(s) en anglais :
Source term estimation
bayesian inference
Monte-Carlo techniques
adaptive algorithms
bayesian inference
Monte-Carlo techniques
adaptive algorithms
Discipline(s) HAL :
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Informatique [cs]/Traitement du signal et de l'image [eess.SP]
Informatique [cs]/Traitement du signal et de l'image [eess.SP]
Résumé en anglais : [en]
In this paper, we present an adaptive algorithm for the estimation of source parameters when a release of pollutant in the atmosphere is observed by a sensor network in complex flow field. Due to the error-based observations, ...
Lire la suite >In this paper, we present an adaptive algorithm for the estimation of source parameters when a release of pollutant in the atmosphere is observed by a sensor network in complex flow field. Due to the error-based observations, inverse statistical methods have to be used to perform an estimation of the parameters (position of the source, time and mass of the release) of interest. However, given the complexity of the dispersion model, even with a Gaussian assumption on the sensor-based errors, direct inversion cannot be done. In order to have quick results, classical MCMC, while accurate, is too slow. We then demonstrate the accuracy of using adaptive techniques such as the AMIS (Population Monte-Carlo based). We finally compare the results with the classical MCMC estimation in term of accuracy and velocity of implementation.Lire moins >
Lire la suite >In this paper, we present an adaptive algorithm for the estimation of source parameters when a release of pollutant in the atmosphere is observed by a sensor network in complex flow field. Due to the error-based observations, inverse statistical methods have to be used to perform an estimation of the parameters (position of the source, time and mass of the release) of interest. However, given the complexity of the dispersion model, even with a Gaussian assumption on the sensor-based errors, direct inversion cannot be done. In order to have quick results, classical MCMC, while accurate, is too slow. We then demonstrate the accuracy of using adaptive techniques such as the AMIS (Population Monte-Carlo based). We finally compare the results with the classical MCMC estimation in term of accuracy and velocity of implementation.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :