Evaluation of Bayesian source estimation methods with Prairie Grass observations and Gaussian plume model: A comparison of likelihood functions and distance measures
Bayesian networks
; Inference engines
; Markov processes
; Meteorology
; Monte Carlo methods
; Approximate Bayesian
; Bayesian inference
; Field experiment
; Markov Chain Monte-Carlo
; Sequential Monte Carlo
; Source estimation
; Atmospheric movements
; algorithm
; atmospheric plume
; atmospheric pollution
; Bayesian analysis
; data set
; dispersion
; emission
; estimation method
; grass
; Markov chain
; maximum likelihood analysis
; Monte Carlo analysis
; performance assessment
; prairie
; vector
; algorithm
; Article
; atmospheric dispersion
; Bayesian learning
; controlled study
; dispersion
; field experiment
; plume
; prairie
; priority journal
; sampling
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:
Source term estimation for atmospheric dispersion deals with estimation of the emission strength and location of an emitting source using all available information, including site description, meteorological data, concentration observations and prior information. In this paper, Bayesian methods for source term estimation are evaluated using Prairie Grass field observations. The methods include those that require the specification of the likelihood function and those which are likelihood free, also known as approximate Bayesian computation (ABC) methods. The performances of five different likelihood functions in the former and six different distance measures in the latter case are compared for each component of the source parameter vector based on Nemenyi test over all the 68 data sets available in the Prairie Grass field experiment. Several likelihood functions and distance measures are introduced to source term estimation for the first time. Also, ABC method is improved in many aspects. Results show that discrepancy measures which refer to likelihood functions and distance measures collectively have significant influence on source estimation. There is no single winning algorithm, but these methods can be used collectively to provide more robust estimates. � 2017
Institute of Public Safety Research, Department of Engineering Physics, Tsinghua University, Beijing, China; School of Engineering, RMIT University, Melbourne, Australia
Recommended Citation:
Wang Y,, Huang H,, Huang L,et al. Evaluation of Bayesian source estimation methods with Prairie Grass observations and Gaussian plume model: A comparison of likelihood functions and distance measures[J]. Atmospheric Environment,2017-01-01,152