Abstract
We apply the Bayesian inference in combination with the Genetic algorithm (GA) to the problem of the atmospheric contaminant source localization. The algorithm input data are the on-line incoming concentrations of released substance registered by sensors network. The proposed reconstruction algorithm is firstly adjusted and tested based on the data from the synthetic experiment. The proposed GA scan 5-dimensional parameters space searching for the contaminant source coordinates (x,y), release strength (Q) and the atmospheric transport dispersion coefficients. Based on the performed tests the most efficient GA configuration is identified. To speed up the algorithm the dynamical termination criteria, founded on the probabilistic requirements regarding the searched parameters value, is proposed. Then, we apply developed algorithm to localize the release source utilizing data from the field tracer experiment conducted in May 2001 at the Kori nuclear site. We demonstrate successful localization of the continuous contamination source in very complicated hilly terrain surrounding the Kori nuclear site. Results indicate the probability of a source to occur at a particular location with a particular release strength.
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References
A. Keats, E. Yee, F.-S. Lien, Bayesian inference for source determination with applications to a complex urban environment. Atmos. Environ. 41, 465–479 (2007). doi:10.1016/j.atmosenv.2006.08.044
J.A. Pudykiewicz, Application of adjoint tracer transport equations for evaluating source parameters. Atmos. Environ. 32, 303–3050 (1998). doi:10.1016/S1352-2310(97)00480-9
G. Johannesson et al., Sequential Monte-Carlo based framework for dynamic data-driven event reconstruction for atmospheric release, in Proceedings of the Joint Statistical Meeting, (American Statistical Association and Cosponsors, Minneapolis, 2005), pp. 73–80
M. Borysiewicz, A. Wawrzynczak, P. Kopka, 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 (IEEE Press, Wroclaw, 2012), pp. 501–508. ISBN 978-83-60810-51-4
M. Borysiewicz, A. Wawrzynczak, P. Kopka, 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). doi:10.2478/v10209-011-0014-9
A. Wawrzynczak, P. Kopka, M. Borysiewicz, Sequential Monte Carlo in Bayesian assessment of contaminant source localization based on the distributed sensors measurements. Lecture Notes in Computer Sciences. PPAM 2013, vol 8385, Part II, Chap. 38, pp. 407–417 (2014). doi:10.1007/978-3-642-55195-6_38
J.H. Holland, Adaptation in Natural and Artificial Systems, 2nd edn. (MIT Press, Cambridge, 1992)
D.E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning (Addison Wesley Longman, London, 2006)
P.J. Fleming, R.C. Purshouse, Genetic algorithms in control systems engineering, in Proceedings of the 12th IFAC World Congress, pp. 383–390 (2001)
R.M. Goodall, K. Michail, J.F. Whidborne, A.C. Zolotas, Optimised configuration of sensing elements for control and fault tolerance applied to an electro-magnetic suspension. Ph.D. thesis, Loughborough University, UK (2009)
P. Rustem (ed.), Genetic algorithms in applications. InTech, Chapters published 21 March 2012 under CC BY 3.0 license (2012). doi:10.5772/2675. ISBN 978-953-51-0400-1
C.T. Allen, S.E. Haupt, Source characterization with a genetic algorithm-coupled dispersion-backward model incorporating SCIPUFF. Department of Meteorology, The Pennsylvania State University (2006). doi:10.1175/JAM2459.1
A.E. Eiben, R. Hinterding, Z. Michalewicz, Parameter control in evolutionary algorithms. IEEE Trans. Evol. Comput. 3(2), 121–141 (1999). doi:10.1109/4235.771166
A. Saremi, T.Y.E. Mekkawy, G.G. Wang, Tuning the parameters of a memetic algorithm to solve vehicle routing problem with backhauls using design of experiments. Int. J. Oper. Res. 4(4), 206–219 (2007)
O. Roeva, S. Fidanova, M. Paprzycki, Influence of the population size on the genetic algorithm performance in case of cultivation process modelling, in Proceedings of the 2013 Federated Conference on Computer Science and Information Systems, pp. 371–376 (2013)
R.I. Sykes et al., PC-SCIPUFF Version 1.2PD Technical Documentation. ARAP report no. 718. Titan Corporation (1998)
A. Gelman, J. Carlin, H. Stern, D. Rubin, Bayesian Data Analysis (Chapman & Hall/CRC, Boca Raton, 2003)
D.B. Turner, Workbook of Atmospheric Dispersion Estimates (Lewis Publishers, Boca Raton, 1994)
F. Pasquill, The estimate of the dispersion of windborne material. Meteorol. Mag. 90(1063), 33–49 (1961)
F.A. Gifford Jr., Atmospheric dispersion calculation using generalized Gaussian plum model. Nucl. Saf. 2(2), 56–59, 67–68 (1960)
A. Wawrzynczak et al., Recognition of the atmospheric contamination source localization with the genetic algorithm, Studia Informatica, UPH, Siedlce (in print) (2015)
A. Wawrzynczak, M. Jaroszyski, M. Borysiewicz, Data-driven genetic algorithm in Bayesian estimation of the abrupt atmospheric contamination source, in Proceedings of the 2014 Federated Conference on Computer Science and Information Systems. Annals of Computer Science and Information Systems, vol. 2, pp. 519–527 (2014). doi:10.15439/2014F272
K.S. Suh, E.H. Kim, W.H. Hwang, H.J. Jeong, M.H. Han, Atmospheric dispersion modeling over the Kori nuclear site, in 11th International Congress of the International Radiation Protection Association, Madrit, Spain (2004)
M.H. Han, E.H. Kim, K.S. Suh et al., Simulation of the dispersion of radioactive effluents over the Kori site using field tracer experiment. J. Nucl. Sci. Technol., Supplement 4, 423–426 (2004)
M.H. Han, E.H. Kim, K.S. Suh et al., Field tracer experiments over nuclear sites for the validation of a Korean real-time atmospheric dispersion and dose assessment system (FADAS). Int. J. Environ. Pollut. 16, 1–6 (2001)
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Wawrzynczak, A., Jaroszynski, M., Borysiewicz, M. (2016). Bayesian-Based Approach to Application of the Genetic Algorithm to Localize the Abrupt Atmospheric Contamination Source. In: Fidanova, S. (eds) Recent Advances in Computational Optimization. Studies in Computational Intelligence, vol 610. Springer, Cham. https://doi.org/10.1007/978-3-319-21133-6_13
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