Assessing sensitivity of source term estimation

Long, Kerrie J.; Haupt, Sue Ellen; Young, George S.

doi:10.1016/j.atmosenv.2010.01.003

Cited by 50 publications

(18 citation statements)

References 24 publications

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“…Mobile sensors can estimate the source term more efficiently as they can be rapidly deployed and collect observational data from more informative locations. Kuroki et al (2010) used an expert system for sensor motion planning, while the genetic algorithm from Long et al (2010) estimated the source term of the dispersing contaminant. Ristic et al (2010) used mobile sensors to estimate the source term of a radiological release.…”

Section: Introductionmentioning

confidence: 99%

“…The most popular approach to STE features a static network of concentration sensors spread over a large region on the ground. Source estimation is then carried out using optimization (Long et al (2010); Thomson et al (2007)) or Bayesian inference algorithms (Keats et al (2007); Senocak et al (2008)) where inferred source parameters are run in a forward ATD model to generate predicted concentrations that are then compared with the data using a cost or likelihood function. A recent study by Platt and Deriggi (2010) based on data from the FFT07 experiment demonstrated some of the limitations of theses approaches when applied to real data.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Bayesian Sensor Motion Planning for Hazardous Source Term Reconstruction

Hutchinson

Wen-hua

2017

IFAC-PapersOnLine

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There has been a strong interest in emergency planning in response to an attack or accidental release of harmful chemical, biological, radiological or nuclear substances. Under such circumstances, it is of paramount importance to determine the location and release rate of the hazardous source to forecast the future harm it may cause and employ methods to minimize the disturbance. In this paper, a sensor data collection strategy is proposed whereby an autonomous mobile sensor is guided to address such a problem with a high degree of accuracy and in a short amount of time. First, the parameters of the release source are estimated using the Markov chain Monte Carlo sampling approach. The most informative manoeuvre from the set of possible choices is then selected using the concept of maximum entropy sampling. Numerical simulations demonstrate the superior performance of the proposed algorithm compared to traditional approaches in terms of estimation accuracy and the number of measurements required.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Bayesian Sensor Motion Planning for Hazardous Source Term Reconstruction

Hutchinson

Wen-hua

2017

IFAC-PapersOnLine

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“…Therefore, this variable has been added to the list of unknown variables to optimize. This method was systematically analyzed for sensitivity to formulation (LONG et al 2010a).…”

Section: Review Of the Uses Of The Ga-var Approachmentioning

confidence: 99%

A Genetic Algorithm Variational Approach to Data Assimilation and Application to Volcanic Emissions

Schmehl

Haupt

Pavolonis

2011

Pure Appl. Geophys.

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Variational data assimilation methods optimize the match between an observed and a predicted field. These methods normally require information on error variances of both the analysis and the observations, which are sometimes difficult to obtain for transport and dispersion problems. Here, the variational problem is set up as a minimization problem that directly minimizes the root mean squared error of the difference between the observations and the prediction. In the context of atmospheric transport and dispersion, the solution of this optimization problem requires a robust technique. A genetic algorithm (GA) is used here for that solution, forming the GA-Variational (GA-Var) technique. The philosophy and formulation of the technique is described here. An advantage of the technique includes that it does not require observation or analysis error covariances nor information about any variables that are not directly assimilated. It can be employed in the context of either a forward assimilation problem or used to retrieve unknown source or meteorological information by solving the inverse problem. The details of the method are reviewed. As an example application, GA-Var is demonstrated for predicting the plume from a volcanic eruption. First the technique is employed to retrieve the unknown emission rate and the steering winds of the volcanic plume. Then that information is assimilated into a forward prediction of its transport and dispersion. Concentration data are derived from satellite data to determine the observed ash concentrations. A case study is made of the March 2009 eruption of Mount Redoubt in Alaska. The GA-Var technique is able to determine a wind speed and direction that matches the observations well and a reasonable emission rate.

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“…Insufficient spatial and temporal resolution of the available contaminant and wind-field observations make source characterization extremely difficult (Allen, when using typical networks of fixed sensors. Given sufficient observational data, however, the problem is feasible as shown by Long, Haupt, and Young (2010). That study demonstrated the use of the Gaussian puff equation as the dispersion model in identical twin numerical experiments applying a genetic algorithm (GA) to back calculate the required source characteristics solely from observations on grids ranging from 8 Â 8 to 2 Â 2 of fixed location concentration sensors.…”

Section: Introductionmentioning

confidence: 98%

UAV navigation by an expert system for contaminant mapping with a genetic algorithm

Kuroki

Young

Haupt

2010

Expert Systems with Applications

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Assessing sensitivity of source term estimation

Cited by 50 publications

References 24 publications

Adaptive Bayesian Sensor Motion Planning for Hazardous Source Term Reconstruction

Adaptive Bayesian Sensor Motion Planning for Hazardous Source Term Reconstruction

A Genetic Algorithm Variational Approach to Data Assimilation and Application to Volcanic Emissions

UAV navigation by an expert system for contaminant mapping with a genetic algorithm

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