A review of source term estimation methods for atmospheric dispersion events using static or mobile sensors
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TLDR
This paper presents a review of techniques used to gain information about atmospheric dispersion events using static or mobile sensors and discusses on the current limitations of the state of the art and recommendations for future research.About:
This article is published in Information Fusion.The article was published on 2017-07-01 and is currently open access. It has received 202 citations till now. The article focuses on the topics: Atmospheric dispersion modeling.read more
Citations
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Journal ArticleDOI
Information-Based Search for an Atmospheric Release Using a Mobile Robot: Algorithm and Experiments
TL;DR: The first experimental result of a joint Bayesian estimation and planning algorithm to guide a mobile robot to collect informative measurements, allowing the source parameters to be estimated quickly and accurately, is presented.
Journal ArticleDOI
Entrotaxis as a strategy for autonomous search and source reconstruction in turbulent conditions
TL;DR: This paper compares the performance and search behaviour of Entrotaxis with the popular Infotaxis algorithm, for searching in sparse and turbulent conditions where typical gradient-based approaches become inefficient or fail, and achieves a faster mean search time.
Journal ArticleDOI
Natural gas fugitive leak detection using an unmanned aerial vehicle: Localization and quantification of emission rate
Levi M. Golston,Nicholas F. Aubut,Michael B. Frish,Shuting Yang,Robert W. Talbot,Christopher Gretencord,James McSpiritt,Mark A. Zondlo +7 more
TL;DR: In this paper, a set of methods for locating and quantifying natural gas leaks using a small unmanned aerial system equipped with a pathintegrated methane sensor is described, supported by a series of over 200 methane release trials covering 51 release location and flow rate combinations.
Journal ArticleDOI
Atmospheric dispersion prediction and source estimation of hazardous gas using artificial neural network, particle swarm optimization and expectation maximization
TL;DR: A fast and accurate dispersion prediction and source estimation method based on artificial neural network (ANN), particle swarm optimization (PSO) and expectation maximization (EM) that can effectively accelerate the process of convergence.
Journal ArticleDOI
Bayesian source term estimation of atmospheric releases in urban areas using LES approach.
TL;DR: A novel source term estimation method is proposed based on LES approach using Bayesian inference that reduces the errors of source location and releasing strength by 77% and 28%, respectively.
References
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Proceedings ArticleDOI
Achievable accuracy in parameter estimation of a Gaussian plume dispersion model
TL;DR: A theoretical analysis of the best achievable accuracy in estimation of Gaussian plume model parameters is presented, illustrating how parameter estimation accuracy depends on sensor measurement accuracy, the density of sensors and the quality of (prior) meteorological advice.
Proceedings ArticleDOI
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Oil spills boundary tracking using Universal Kriging and Model Predictive Control by UAV
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TL;DR: In this article, a UAV is used to estimate the unknown situation of oil dispersion for just one unmanned aerial vehicle (UAV) to predict and track the objective boundary automatically.
Proceedings ArticleDOI
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TL;DR: An adaptive sampling strategy for efficient simultaneous tracking of multiple concentration levels of an atmospheric plume by a team of cooperating unmanned aerial vehicles (UAVs) is proposed.
Bayesian Inversion of Concentration Data for an Unknown Number of Contaminant Sources
E. Yee,Drdc Suffield +1 more
TL;DR: In this paper, a Bayesian probabilistic inferential framework is used to derive the posterior probability density function for the number of sources and for the parameters (e.g., location, emission rate, release duration) that characterize each source.