气候变化领域集成服务门户(CCPortal): Sequential Monte Carlo sampler applied to source term estimation in complex atmospheric environments

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DOI	10.1016/j.atmosenv.2021.118822
	Sequential Monte Carlo sampler applied to source term estimation in complex atmospheric environments
	Septier F.; Armand P.; Duchenne C.
发表日期	2022
ISSN	1352-2310
卷号	269
英文摘要	The accurate and rapid reconstruction of a pollution source represents an important but challenging problem. Several strategies have been proposed to tackle this issue among which we find the Bayesian solutions that have the interesting ability to provide a complete characterization of the source parameters through their posterior probability density function. However, these existing techniques have certain limitations such as their computational complexity, the required model assumptions, their difficulty to converge, the sensitive choice of model/algorithm parameters which clearly limit their easy use in practical scenarios. In this paper, to overcome these limitations, we propose a novel Bayesian solution based on a general and flexible population-based Monte Carlo algorithm, namely the sequential Monte Carlo sampler. Owing to its full adaptivity through the learning process, the main advantage of such an algorithm lies in its capability to be used without requiring any specific assumptions on the underlying statistical model and also without requiring from the user any difficult choices of certain parameter values. The performance of the proposed inference strategy is assessed using twin experiments in complex built-up environments. © 2021 Elsevier Ltd
关键词	Adaptivity Bayesian approach Dispersion model Sequential importance sampling Source term estimation Synthetic example
语种	英语
scopus关键词	Importance sampling; Parameter estimation; Probability density function; Adaptivity; Bayesian approaches; Bayesian solution; Complex atmospheric environments; Dispersion models; Pollution sources; Sequential importance sampling; Sequential Monte Carlo; Source term estimation; Synthetic example; Bayesian networks; Bayesian analysis; estimation method; Monte Carlo analysis; pollutant source; reconstruction; source parameters; air monitoring; airflow; algorithm; Article; atmospheric dispersion; atmospheric transport; Bayesian learning; canopy; concentration (parameter); controlled study; digital twin; environmental temperature; geographic elevation; humidity; Markov chain; measurement accuracy; measurement error; Monte Carlo method; noise measurement; plume dispersion; sequential analysis; source term estimation; statistical model; three-dimensional imaging; time factor; uncertainty; urban area; variance; wind speed
来源期刊	ATMOSPHERIC ENVIRONMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/248117
作者单位	Université Bretagne Sud, LMBA UMR CNRS 6205, Vannes, F-56000, France; CEA, DAM, DIF, Arpajon, F-91297, France
推荐引用方式 GB/T 7714	Septier F.,Armand P.,Duchenne C.. Sequential Monte Carlo sampler applied to source term estimation in complex atmospheric environments[J],2022,269.
APA	Septier F.,Armand P.,&Duchenne C..(2022).Sequential Monte Carlo sampler applied to source term estimation in complex atmospheric environments.ATMOSPHERIC ENVIRONMENT,269.
MLA	Septier F.,et al."Sequential Monte Carlo sampler applied to source term estimation in complex atmospheric environments".ATMOSPHERIC ENVIRONMENT 269(2022).