BME analysis of neural network residual data from the Chernobyl fallout: bayesian and non-bayesian approaches
Détails
ID Serval
serval:BIB_02E77A01E386
Type
Actes de conférence (partie): contribution originale à la littérature scientifique, publiée à l'occasion de conférences scientifiques, dans un ouvrage de compte-rendu (proceedings), ou dans l'édition spéciale d'un journal reconnu (conference proceedings).
Collection
Publications
Institution
Titre
BME analysis of neural network residual data from the Chernobyl fallout: bayesian and non-bayesian approaches
Titre de la conférence
geoENV III: Geostatistics for Environmental Applications Proceedings of the 3rd European Conference on Geostatistics for Environmental Applications, Avignon, France
ISBN
978-94-010-0810-5
ISSN-L
0924-1973
Statut éditorial
Publié
Date de publication
2000
Peer-reviewed
Oui
Editeur⸱rice scientifique
Monestiez P., Allard D., Froidevaux R.
Volume
11
Pages
509-510
Langue
anglais
Notes
Christakos2000
Résumé
Radioactively contaminated territories after the Chernobyl accident
are characterized by non-stationary trends and soft (uncertain) information
about the average concentration of radionuclide in soil. Large-scale
decision-oriented mapping in this situation involves using the Neural
Network method to determine the general non-linear trends of the
data, and the BME method to analyze the hard and soft residual information
generated after the mean trend is removed from the data. In this
work we explore different approaches to map the residual soft data,
which include a Bayesian and a non-Bayesian framework. These approaches
are illustrated by means of the Cs137 mapping case study in Briansk,
Russia
are characterized by non-stationary trends and soft (uncertain) information
about the average concentration of radionuclide in soil. Large-scale
decision-oriented mapping in this situation involves using the Neural
Network method to determine the general non-linear trends of the
data, and the BME method to analyze the hard and soft residual information
generated after the mean trend is removed from the data. In this
work we explore different approaches to map the residual soft data,
which include a Bayesian and a non-Bayesian framework. These approaches
are illustrated by means of the Cs137 mapping case study in Briansk,
Russia
Création de la notice
25/11/2013 19:02
Dernière modification de la notice
20/08/2019 13:25
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