Abstract
In this paper, the Generalized Extremal Optimization (GEO) algorithm is combined with the Sandpile model to localize the airborne contaminant source based on the contaminant concentration’s spatial distribution. The GEO algorithm scans the proposed model’s solution space to find the contamination source by comparing the Sandpile model output with the contaminant distribution over the considered area. The comparison is made by evaluating the assessment function considering the differences between the distribution of the sand grains from the Sandpile model and contaminant concentrations reported by the sensor network monitoring the considered area. The evolution of the sand grains in the Sandpile model is realized by the cellular automata cells. The proposed GEO-Sandpile localization model efficiency is verified using the synthetic contaminant concentration data generated by the Gaussian dispersion model: conducted test cases presented in this paper covered the various wind directions, and release source positions. Obtained results support the statement that the proposed algorithm can, with acceptable accuracy, localize the contaminant source based only on the sparse-point concentrations of the released substance.
Keywords
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Aegerter, C.M.: A sandpile model for the distribution of rainfall? Phys. A 319, 1–10 (2003)
Bak, P.: How Nature Works: The Science of Self-Organized Criticality, 1st edn. Springer, Heidelberg (1999)
Bak, P., Tang, C.: Earthquakes as a self-organized critical phenomenon. J. Geophys. Res. 94(B11), 15635–15637 (1989)
Bak, P., Tang, C., Wiesenfeld, K.: Self-organized criticality: an explanation of the 1/f noise. Phys. Rev. Lett. 59(4), 381–384 (1987)
Bjorner, A., Lovász, L., Shor, W.: Chip-firing games on graphs. Eur. J. Combin. 12, 283–291 (1991)
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, Wroclaw (2012). ISBN 978-83-60810-51-4
Chan, Y., Marckert, J.F., Selig, T.: A natural stochastic extension of the sandpile model on a graph. J. Combin. Theory Ser. A 120, 1913–1928 (2013)
Chapman, S.C., Dendy, R.O., Rowlands, G.: A sandpile model with dual scaling regimes for laboratory, space and astrophysical plasmas. Phys. Plasmas 6, 4169 (1999)
D\(\acute{e}\)s\(\acute{e}\)rable, D., Dupont, P., Hellou, M., Kamali-Bernard, S.: Cellular automata in complex matter. Complex Syst. 20(1), 67–91 (2011)
Formenti, E., Pham, T.V., Duong Phan, T.H., Thu, T.: Fixed-point forms of the parallel symmetric sandpile model. Theoret. Comput. Sci. 533, 1–14 (2014)
Frette, V., Christensen, K., Malthe-Sørenssen, A., Feder, J., Jøssang, T., Meakin, P.: Avalanche dynamics in a pile of rice. Nature 379, 49–52 (1996)
Galski, R.L., de Sousa, F.L., Ramos, F.M., Muraoka, I.: Spacecraft thermal design with the genrelized extremal optimization algorithm. In: Inverse Problems, Design and Optimization Symposium, Brazil (2004)
Goles, E., Morvan, M., Phan, H.D.: Sandpiles and order structure of integer partitions. Discrete Appl. Math. 117, 51–64 (2002)
Hutchinson, M., Oh, H., Chen, W.H.: A review of source term estimation methods for atmospheric dispersion events using static or mobile sensors. Inform. Fusion 36, 130–148 (2017)
Hesse, J., Gross, T.: Self-organized criticality as a fundamental property of neural systems. Front. Syst. Neurosci. 8, 166 (2014)
Bhaumik, H., Santra, S.B.: Stochastic sandpile model on small-world networks: scaling and crossover. Phys. A: Stat. Mech. Appl. 511, 258–370 (2018)
Kopka, P., Wawrzynczak, A.: Framework for stochastic identification of atmospheric contamination source in an urban area. Atmos. Environ. 195, 63–77 (2018)
Latapy, M., Mataci, R., Morvan, M., Phan, H.D.: Structure of some sandpiles model. Theoret. Comput. Sci. 262, 525–556 (2001)
Latapy, M., Phan, H.D.: The lattice structure of chip firing games. Phys. D 155, 69–82 (2000)
Marchel, L.: Zastosowanie modelu ‘Sandpile’ w procesie optymalizacji. Master thesis, supervisor: Szaban M. (2020). (in Polish)
Parsaeifard, B., Moghimi-Araghi, S.: Controlling cost in sandpile models through local adjustment of drive. Phys. A 534, 122185 (2019)
Ricotta, C., Avena, G., Marchetti, M.: The flaming sandpile: self-organized criticality and wildfires. Ecol. Model. 119(1), 73–77 (1999)
Rossin, D., Cori, R.: On the sandpile group of dual graphs. Eur. J. Combin. 21(4), 447–459 (2000)
Rothman, D., Grotzinger, J., Flemings, P.: Scaling in turbidite deposition. J. Sediment. Res. 64(1a), 59–67 (1994)
De Sousa, F.L., Ramos, F.M., Paglione, P., Girardi, R.M.: New stochastic algorithm for design optimization. AIAA J. 41(9), 1808–1818 (2003)
De Sousa, F.L., Vlassov, V., Ramos, F.M.: Generalized extremal optimization: an application in heat pipe design. Appl. Math. Modell. 28, 911–931 (2004)
De Sousa, F.L., Ramos, F.M., Soeiro F.J.C.P., Silva Neto, A.J.: Application of the generalized extremal optimizayion algorithm to an inverse radiative transfer problem. In: Proceedings of the 5th International Conference on Inverse Problems in Engineering: Theory and Practice, Cambridge, UK, 11–15 July 2005 (2005)
Wawrzynczak, A., Danko, J., Borysiewicz, M.: Lokalizacja zrodla zanieczyszczen atmosferycznych za pomoca algorytmu roju czastek. Acta Scientarium Polonorum Adm. Locorum 13(4), 71–91 (2014). (in Polish)
Wawrzynczak, A., Berendt-Marchel, M.: Can the artificial neural network be applied to estimate the atmospheric contaminant transport? In: Dimov, I., Fidanova, S. (eds.) HPC 2019. SCI, vol. 902, pp. 132–142. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-55347-0_12. ISBN 978-3-030-55346-3
Wawrzynczak, A., Berendt-Marchel, M.: Computation of the airborne contaminant transport in urban area by the artificial neural network. In: Krzhizhanovskaya, V.V., et al. (eds.) ICCS 2020, Part II. LNCS, vol. 12138, pp. 401–413. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50417-5_30. ISBN 978-3-030-50416-8
Wawrzynczak, A., Kopka, P., Borysiewicz, M.: 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.) PPAM 2013. LNCS, vol. 8385, pp. 407–417. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-55195-6_38
Wolfram, S.: A New Kind of Science. Wolfram Media (2002)
Xie, D., Luo, Z., Yu, F.: The computing of the optimal power consumption of semi-track air-cushion vehicle using hybrid generalized extremal optimization. Appl. Math. Model. 33, 2831–2844 (2009)
Zannetti, P.: Gaussian models. In: Zannetti, P. (ed.) Air Pollution Modeling, pp. 141–183. Springer, Boston (1990). https://doi.org/10.1007/978-1-4757-4465-1_7
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Szaban, M., Wawrzynczak, A., Berendt-Marchel, M., Marchel, L. (2021). Application of the Generalized Extremal Optimization and Sandpile Model in Search for the Airborne Contaminant Source. In: Malyshkin, V. (eds) Parallel Computing Technologies. PaCT 2021. Lecture Notes in Computer Science(), vol 12942. Springer, Cham. https://doi.org/10.1007/978-3-030-86359-3_36
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