A sparse grid based Bayesian method for contaminant source identification

Advances in Water Resources

Volumn 37, Issue , 2012, Pages 1-9

  Zeng, Lingzao ^a   Shi, Liangsheng ^b   Zhang, Dongxiao ^c   Wu, Laosheng ^a  

^a ZHEJIANG UNIVERSITY   (China)

^b WUHAN UNIVERSITY   (China)

^c PEKING UNIVERSITY   (China)

Author keywords

Inverse problem; MCMC; Source identification; Sparse grid interpolation; Stochastic collocation method

Indexed keywords

ADAPTIVE SCHEME; BAYESIAN APPROACHES; BAYESIAN METHODS; COMPUTATIONAL BURDEN; COMPUTATIONAL COSTS; CONTAMINANT SOURCES; MARKOV CHAIN MONTE CARLO METHOD; MCMC; MCMC METHOD; MCMC SAMPLING; POLYNOMIAL FORM; PROPOSAL DISTRIBUTION; SOURCE IDENTIFICATION; SPARSE GRID; STOCHASTIC COLLOCATION METHOD; SURROGATE SYSTEMS; TENSOR PRODUCTS; UNKNOWN PARAMETERS;

BAYESIAN NETWORKS; CONTAMINATION; IDENTIFICATION (CONTROL SYSTEMS); INTERPOLATION; INVERSE PROBLEMS; MARKOV PROCESSES; NUMERICAL METHODS; RANDOM VARIABLES; STOCHASTIC SYSTEMS; TENSORS;

MONTE CARLO METHODS;

ACCURACY ASSESSMENT; BAYESIAN ANALYSIS; INVERSE PROBLEM; MARKOV CHAIN; MONTE CARLO ANALYSIS; PARAMETERIZATION; POLLUTANT SOURCE; POLLUTANT TRANSPORT; STOCHASTICITY;

EID: 84855220967     PISSN: 03091708     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.advwatres.2011.09.011     Document Type: Article

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