A review of source term estimation methods for atmospheric dispersion events using static or mobile sensors
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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 Article
Multi UAV Coordination for Tracking the Dispersion of a Contaminant Cloud in an Urban Region
TL;DR: In this article, the shape of a cloud is modeled using splinegon and the movement of the cloud is tracked using an observer using the output of the observer is used in the path planning of the UAVs.
Book ChapterDOI
Sequential Monte Carlo in Bayesian Assessment of Contaminant Source Localization Based on the Sensors Concentration Measurements
TL;DR: Bayesian approach occurs as a powerful tool being able to combine observed data along with prior knowledge to gain a current understanding of unknown model parameters.
Journal ArticleDOI
Autonomous search for a diffusive source in an unknown structured environment
TL;DR: In this article, a framework for autonomous search for a diffusive emitting source of a tracer (e.g., aerosol, gas) in an environment with an unknown map of randomly placed and shaped obstacles is presented.
Journal ArticleDOI
A point-source reconstruction from concentration measurements in low-wind stable conditions
TL;DR: In this article, an inversion technique is proposed to reconstruct an elevated point emission source of known height of atmospheric trace species from a finite number of concentration measurements in low-wind stable conditions.
Journal IssueDOI
‘Bayesian source detection and parameter estimation of a plume model based on sensor network measurements’ by C. Huang et al.: Discussion 1
Chunfeng Huang,Tailen Hsing,Noel A Cressie,Auroop R. Ganguly,Vladimir Protopopescu,Nageswara S. V. Rao +5 more
TL;DR: In this article, the authors considered a network of sensors that measure the intensities of a complex plume composed of multiple absorption-diffusion source components and addressed the problem of estimating the plume parameters, including the spatial and temporal source origins and the parameters of the diffusion model for each source, based on a sequence of sensor measurements.