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.
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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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