Abstract
Accidental atmospheric releases of hazardous material pose great risks to human health and the environment. In this context it is valuable to develop the emergency action support system, which can quickly identify probable location and characteristics of the release source based on the measurement of dangerous substance concentration by the sensors network. In this context 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.
We have applied the methodology combining Bayesian inference with Sequential Monte Carlo (SMC) to the problem of the atmospheric contaminant source localization. The algorithm input data are the on-line arriving concentrations of given substance registered by the distributed sensor’s network.
We have proposed the different version of the Hybrid SMC along with Markov Chain Monte Carlo (MCMC) algorithms and examined its effectiveness to estimate the probabilistic distributions of atmospheric release parameters. The proposed algorithms scan 5-dimensional parameters’ space searching for the contaminant source coordinates, release strength and atmospheric transport dispersion coefficients.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Watzenig, D.: Bayesian inference for inverse problems - statistical inversion. Elektrotech. Informationstechnik 124(7–8), 240–247 (2007)
Senocak, I., Hengartner, N.W., Short, M.B., Daniel, W.B.: Stochastic event reconstruction of atmospheric contaminant dispersion using Bayesian inference. Atmos. Environ. 42(33), 7718–7727 (2008)
Borysiewicz, M., Wawrzynczak, A., Kopka, P.: Stochastic algorithm for estimation of the model’s unknown parameters via Bayesian inference. In: Proceedings of the Federated Conference on Computer Science and Information Systems, pp. 501–508. IEEE Press (2012)
Borysiewicz, M., Wawrzynczak, A., Kopka, P.: Bayesian-based methods for the estimation of the unknown model’s parameters in the case of the localization of the atmospheric contamination source. Found. Comput. Decis. Sci. 37(4), 253–270 (2012)
Sykes, R.I. et al.: PC-SCIPUFF Version 1.2PD Technical Documentation. ARAP Report No. 718. Titan Corporation (1998)
Turner, D.B.: Workbook of Atmospheric Dispersion Estimates. Lewis Publishers, USA (1994)
Panofsky, H.A., Dutton, J.A.: Atmospheric Turbulence. John Wiley, New York (1984)
Gelman, A., Carlin, J., Stern, H., Rubin, D.: Bayesian Data Analysis. Chapman & Hall/CRC, Boca Raton (2003)
Gilks, W., Richardson, S., Spiegelhalter, D.: Markov Chain Monte Carlo in Practice. Chapman & Hall/CRC, Boca Raton (1996)
Doucet, A., de Freitas, J.F.G., Gordon, N.J.: Sequential Monte Carlo Methods in Practice. Springer, New York (2001)
Liu, J.S.: Monte Carlo Strategies in Scientific Computing. Springer, New York (2001)
Gordon, N.J., Salmond, D.J., Smith, A.F.M.: Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEEE Proc. Radar Signal Process. 140(2), 107–113 (1993)
Acknowledgments
This work was supported by the Welcome Programme of the Foundation for Polish Science operated within the European Union Innovative Economy Operational Programme 2007-2013 and by the EU and MSHE grant nr POIG.02.03.00-00-013/09. The work was supported by the project VI.B.08 under the NCBiR national programme: “Improving labour and safety conditions: 2nd stage".
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wawrzynczak, A., Kopka, P., Borysiewicz, M. (2014). Sequential Monte Carlo in Bayesian Assessment of Contaminant Source Localization Based on the Sensors Concentration Measurements. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Waśniewski, J. (eds) Parallel Processing and Applied Mathematics. PPAM 2013. Lecture Notes in Computer Science(), vol 8385. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55195-6_38
Download citation
DOI: https://doi.org/10.1007/978-3-642-55195-6_38
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-55194-9
Online ISBN: 978-3-642-55195-6
eBook Packages: Computer ScienceComputer Science (R0)