A review of source term estimation methods for atmospheric dispersion events using static or mobile sensors
TL;DR: 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.
Citations
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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.
Abstract: Finding the location and strength of an unknown hazardous release is of paramount importance in emergency response and environmental monitoring; thus, it has been an active research area for several years known as source term estimation (STE). This paper presents 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. The estimation is performed recursively using Bayes’ theorem, where uncertainties in the meteorological and dispersion parameters are considered and the intermittent readings from a low-cost gas sensor are addressed by a novel likelihood function. The planning strategy is designed to maximize the expected utility function based on the estimated information gain of the source parameters. Subsequently, this paper presents the first experimental result of such a system in turbulent, diffusive conditions, in which a ground robot equipped with the low-cost gas sensor responds to the hazardous source simulated by incense sticks. The experimental results demonstrate the effectiveness of the proposed estimation and search algorithm for STE based on the mobile robot and the low-cost sensor.
68 citations
Cites background or methods from "A review of source term estimation ..."
...1) Inspired by the literature on STE [6], an informationbased search algorithm is developed to accommodate the...
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...concentration sensors and meteorological stations as reviewed in [6] and [11]....
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...Potential responses to a harmful atmospheric release include mapping, boundary tracking, source localization, or source term estimation/reconstruction (STE) [6]....
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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.
63 citations
Cites background from "A review of source term estimation ..."
...Reconstruction of the source term enables prediction of the future extent of hazardous contamination, with applications in emergency response following an accidental or deliberate release of harmful chemical, biological, radiological or nuclear (CBRN) material [8]....
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...The latter falls into the domain of source term estimation, reviewed in [8]....
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23 Aug 2018
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.
Abstract: We describe a set of methods for locating and quantifying natural gas leaks using a small unmanned aerial system equipped with a path-integrated methane sensor. The algorithms are developed as part of a system to enable the continuous monitoring of methane, supported by a series of over 200 methane release trials covering 51 release location and flow rate combinations. The system was found throughout the trials to reliably distinguish between cases with and without a methane release down to 2 standard cubic feet per hour (0.011 g/s). Among several methods evaluated for horizontal localization, the location corresponding to the maximum path-integrated methane reading performed best with a mean absolute error of 1.2 m if the results from several flights are spatially averaged. Additionally, a method of rotating the data around the estimated leak location according to the wind is developed, with the leak magnitude calculated from the average crosswind integrated flux in the region near the source location. The system is initially applied at the well pad scale (100–1000 m2 area). Validation of these methods is presented including tests with unknown leak locations. Sources of error, including GPS uncertainty, meteorological variables, data averaging, and flight pattern coverage, are discussed. The techniques described here are important for surveys of small facilities where the scales for dispersion-based approaches are not readily applicable.
53 citations
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.
48 citations
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.
48 citations
Cites methods from "A review of source term estimation ..."
...To solve the inverse problem, various algorithms have been proposed [1,2], among which the optimization method and Bayesian inference are two dominant approaches in STE problem [3,4]....
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...Here, we assign the simplest and probably most frequently used form, the Gaussian distribution [3]....
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References
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TL;DR: The information deviation between any two finite measures cannot be increased by any statistical operations (Markov morphisms) and is invarient if and only if the morphism is sufficient for these two measures as mentioned in this paper.
Abstract: The information deviation between any two finite measures cannot be increased by any statistical operations (Markov morphisms). It is invarient if and only if the morphism is sufficient for these two measures
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