A review of source term estimation methods for atmospheric dispersion events using static or mobile sensors

Hutchinson, Michael; Oh, Hyondong; Chen, Wenhua

doi:10.1016/j.inffus.2016.11.010

Cited by 270 publications

(153 citation statements)

References 145 publications

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“…Then the best position of each particle so far (pb i ) and the best position of all particles (gb) as well as their fitness (pb f it i and gb f it) are initialized: the initial value of the ith particle is set as pb i , and the best pb i is set as gb. After that, the velocities and positions of all particles are updated according to Equation (3).…”

Section: Solution Algorithmmentioning

confidence: 99%

Hazardous Source Estimation Using an Artificial Neural Network, Particle Swarm Optimization and a Simulated Annealing Algorithm

et al. 2018

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Locating and quantifying the emission source plays a significant role in the emergency management of hazardous gas leak accidents. Due to the lack of a desirable atmospheric dispersion model, current source estimation algorithms cannot meet the requirements of both accuracy and efficiency. In addition, the original optimization algorithm can hardly estimate the source accurately, because of the difficulty in balancing the local searching with the global searching. To deal with these problems, in this paper, a source estimation method is proposed using an artificial neural network (ANN), particle swarm optimization (PSO), and a simulated annealing algorithm (SA). This novel method uses numerous pre-determined scenarios to train the ANN, so that the ANN can predict dispersion accurately and efficiently. Further, the SA is applied in the PSO to improve the global searching ability. The proposed method is firstly tested by a numerical case study based on process hazard analysis software (PHAST), with analysis of receptor configuration and measurement noise. Then, the Indianapolis field case study is applied to verify the effectiveness of the proposed method in practice. Results demonstrate that the hybrid SAPSO algorithm coupled with the ANN prediction model has better performances than conventional methods in both numerical and field cases.

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Section: Solution Algorithmmentioning

confidence: 99%

Hazardous Source Estimation Using an Artificial Neural Network, Particle Swarm Optimization and a Simulated Annealing Algorithm

et al. 2018

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“…Source term estimation (STE) is most commonly performed using an array of static sensors on the ground. Formulated as an inverse problem, Bayesian inference or optimisation algorithms are used to fuse pointwise concentration observations of the hazard with hypothesised predictions from a model (Hutchinson et al, ). Although there are some benefits, such as early detection in areas of interest with an existing network in place, static sensors have several drawbacks, which are overcome by mobile deployment.…”

Section: Introductionmentioning

confidence: 99%

Source term estimation of a hazardous airborne release using an unmanned aerial vehicle

Hutchinson

Liu

Chen

2018

Journal of Field Robotics

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Gaining information about an unknown gas source is a task of great importance with applications in several areas, including responding to gas leaks or suspicious smells, quantifying sources of emissions, or in an emergency response to an industrial accident or act of terrorism. In this paper, a method to estimate the source term of a gaseous release using measurements of concentration obtained from an unmanned aerial vehicle (UAV) is described. The source term parameters estimated include the three‐dimensional location of the release, its emission rate and other important variables needed to forecast the spread of the gas using an atmospheric transport and dispersion model. The parameters of the source are estimated by fusing concentration observations from a gas detector on‐board the aircraft, with meteorological data and an appropriate model of dispersion. Two models are compared in this paper, both derived from analytical solutions to the advection–diffusion equation. Bayes’ theorem, implemented using a sequential Monte Carlo algorithm, is used to estimate the source parameters to take into account the large uncertainties in the observations and formulated models. The system is verified with novel, outdoor, fully automated experiments, where observations from the UAV are used to estimate the parameters of a diffusive source. The estimation performance of the algorithm is assessed subject to various flight path configurations and wind speeds. Observations and lessons learned during these unique experiments are discussed, and areas for future research are identified.

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“…Observed concentration, deposition, or other functions of the atmospheric pollutants such as aerosol optical thickness mea-10 sured by satellite instruments can be used to estimate some combination of source location, strength, and temporal evolution using various source term estimation (STE) methods (Bieringer et al, 2017;Hutchinson et al, 2017). Among the applications, the recent Fukushima Dai-ichi Nuclear Power Plant accidents saw the most implementations of the STE methods to estimate the radionuclide releases.…”

Section: Introductionmentioning

confidence: 99%

Weak-constraint inverse modeling using HYSPLIT Lagrangian dispersion model and Cross Appalachian Tracer Experiment (CAPTEX) observations – Effect of including model uncertainties on source term estimation

Chai¹,

Stein²,

Ngan³

2018

Preprint

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Abstract.A HYSPLIT inverse system that is based on variational data assimilation and a Lagrangian dispersion transfer coefficient matrix (TCM) is evaluated using the Cross Appalachian Tracer Experiment (CAPTEX) data collected from six controlled releases. For simplicity, the initial tests are applied to release 2 for which the HYSPLIT has the best performance. Before introducing model uncertainty terms, the tests using concentration differences in the cost function results in severe underestimation while those using logarithm concentrations differences results in overestimation of the release rate. Adding model uncertainty 5 terms improves results for both choices of the metric variables in the cost function. A cost function normalization scheme is later introduced to avoid spurious minimal source term solutions when using logarithm concentration differences. The scheme is effective in eliminating the spurious solutions and it also helps to improve the release estimates for both choices of the metric variables. The tests also show that calculating logarithm concentration differences generally yield better results than calculating concentration differences and the estimates are more robust for a reasonable range of model uncertainty parameters. This is 10 further confirmed with nine ensemble HYSPLIT runs in which meteorological fields were generated with varying planetary boundary layer (PBL) schemes. In addition, it is found that the emission estimate using a combined TCM by taking the average or median values of the nine TCMs is similar to the median of the nine estimates using each of the TCMs individually. The inverse system is then applied to the other CAPTEX releases with a fixed set of observational and model uncertainty parameters and the largest relative error among the six releases is 53.3%. At last, the system is tested for its capability to find a 15 single source location as well as its source strength. In these tests, the location and strength that yield the best match between the predicted and the observed concentrations are considered as the inverse modeling results. The estimated release rates are mostly not as good as the cases in which the exact release locations are assumed known, but they are all within a factor of 3 for all the six releases. However, the estimated location may have large errors. 1Geosci. Model Dev. Discuss., https://doi

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A review of source term estimation methods for atmospheric dispersion events using static or mobile sensors

Cited by 270 publications

References 145 publications

Hazardous Source Estimation Using an Artificial Neural Network, Particle Swarm Optimization and a Simulated Annealing Algorithm

Hazardous Source Estimation Using an Artificial Neural Network, Particle Swarm Optimization and a Simulated Annealing Algorithm

Source term estimation of a hazardous airborne release using an unmanned aerial vehicle

Weak-constraint inverse modeling using HYSPLIT Lagrangian dispersion model and Cross Appalachian Tracer Experiment (CAPTEX) observations – Effect of including model uncertainties on source term estimation

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