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A novel deep learning simulation to predict radon activity concentration in soil layers

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Abstract

Radon is an isotope gas that has no color or smell. According to the World Health Organization, it is the second most important cause of pulmonary cancer after the use of cigarette smoking. In this paper, we propose a new deep learning model to predict the radon (Rn-222) activity concentration in soil layers by combining different mechanisms of transport including diffusion, advection, decay and generation mechanisms. This work deals with the use of the so-called deep neural network to solve the transport equation described in a one-dimensional case under the effect of soil layer characteristics such as diffusion coefficient, porosity and initial radon concentration. In the first, This model is validated by the analytical solution. Then a comparison of the radon concentration by the analytical solution and the proposed deep learning model is performed and shows a good correlation. Three simulated and compared scenarios show that the generation and advection mechanisms can increase the radon activity concentration at the ground surface.

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Authors and Affiliations

  1. Department of Physics, Ibn Tofail University, University Campus, P.Box 133, 14000, Kenitra, Morocco

    H. Bezzout & H. El Faylali

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  1. H. Bezzout
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  2. H. El Faylali
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Correspondence to H. Bezzout.

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Bezzout, H., El Faylali, H. A novel deep learning simulation to predict radon activity concentration in soil layers. J Radioanal Nucl Chem 332, 457–465 (2023). https://doi.org/10.1007/s10967-022-08735-x

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