DOI: 10.1016/j.atmosenv.2015.10.026
Scopus记录号: 2-s2.0-84945276322
论文题名: An adaptive Bayesian inference algorithm to estimate the parameters of a hazardous atmospheric release
作者: Rajaona H ; , Septier F ; , Armand P ; , Delignon Y ; , Olry C ; , Albergel A ; , Moussafir J
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2015
卷: 122 起始页码: 748
结束页码: 762
语种: 英语
英文关键词: Adaptive multiple importance sampling
; Bayesian inference
; Monte-Carlo techniques
; Source term estimation
Scopus关键词: Algorithms
; Bayesian networks
; Importance sampling
; Inference engines
; Inverse problems
; Iterative methods
; Markov processes
; Adaptive processing
; Atmospheric release
; Bayesian inference
; Markov chain monte carlo algorithms
; Monte Carlo techniques
; Probabilistic approaches
; Sampling efficiency
; Source term estimation
; Monte Carlo methods
; air sampling
; algorithm
; atmospheric pollution
; Bayesian analysis
; Gaussian method
; hazard assessment
; Markov chain
; Monte Carlo analysis
; pollutant source
; pollution monitoring
; probability
; sensor
; air sampling
; algorithm
; Article
; atmospheric dispersion
; Bayes theorem
; concentration (parameters)
; exhaust gas
; kernel method
; methodology
; Monte Carlo method
; priority journal
; probability
; recycling
Scopus学科分类: Environmental Science: Water Science and Technology
; Earth and Planetary Sciences: Earth-Surface Processes
; Environmental Science: Environmental Chemistry
英文摘要: In the eventuality of an accidental or intentional atmospheric release, the reconstruction of the source term using measurements from a set of sensors is an important and challenging inverse problem. A rapid and accurate estimation of the source allows faster and more efficient action for first-response teams, in addition to providing better damage assessment.This paper presents a Bayesian probabilistic approach to estimate the location and the temporal emission profile of a pointwise source. The release rate is evaluated analytically by using a Gaussian assumption on its prior distribution, and is enhanced with a positivity constraint to improve the estimation. The source location is obtained by the means of an advanced iterative Monte-Carlo technique called Adaptive Multiple Importance Sampling (AMIS), which uses a recycling process at each iteration to accelerate its convergence.The proposed methodology is tested using synthetic and real concentration data in the framework of the Fusion Field Trials 2007 (FFT-07) experiment. The quality of the obtained results is comparable to those coming from the Markov Chain Monte Carlo (MCMC) algorithm, a popular Bayesian method used for source estimation. Moreover, the adaptive processing of the AMIS provides a better sampling efficiency by reusing all the generated samples. © 2015 Elsevier Ltd. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/81425
Appears in Collections: 气候变化事实与影响
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作者单位: Institut Mines-Télécom/Télécom Lille, LAGIS UMR CNRS 8219, Villeneuve-d'Ascq, France; CEA-DAM, DIF, Arpajon, France; ARIA Technologies, 8 rue de la Ferme, Boulogne-Billancourt, France
Recommended Citation:
Rajaona H,, Septier F,, Armand P,et al. An adaptive Bayesian inference algorithm to estimate the parameters of a hazardous atmospheric release[J]. Atmospheric Environment,2015-01-01,122