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Determination and mapping the spatial distribution of radioactivity of natural spring water in the Eastern Black Sea Region by using artificial neural network method

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Abstract

In this study, radiological distribution of gross alpha, gross beta, 226Ra, 232Th, 40K, and 137Cs for a total of 40 natural spring water samples obtained from seven cities of the Eastern Black Sea Region was determined by artificial neural network (ANN) method. In the ANN method employed, the backpropagation algorithm, which estimates the backpropagation of the errors and results, was used. In the structure of ANN, five input parameters (latitude, longitude, altitude, major soil groups, and rainfall) were used for natural radionuclides and four input parameters (latitude, longitude, altitude, and rainfall) were used for artificial radionuclides, respectively. In addition, 75 % of the total data were used as the data of training and 25 % of them were used as test data in order to reveal the structure of each radionuclide. It has been seen that the results obtained explain the radiographic structure of the region very well. Spatial interpolation maps covering the whole region were created for each radionuclide including spots not measured by using these results. It has been determined that artificial neural network method can be used for mapping the spatial distribution of radioactivity with this study, which is conducted for the first time for the Black Sea Region.

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Authors and Affiliations

  1. Department of Physics, Faculty of Art and Science,, Artvin Çoruh University, 08000, Artvin, Turkey

    Cafer Mert Yeşilkanat

  2. Energy Systems Engineering, Faculty of Engineering, Artvin Çoruh University, 08000, Artvin, Turkey

    Yaşar Kobya

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  1. Cafer Mert Yeşilkanat
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  2. Yaşar Kobya
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Correspondence to Yaşar Kobya.

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Yeşilkanat, C.M., Kobya, Y. Determination and mapping the spatial distribution of radioactivity of natural spring water in the Eastern Black Sea Region by using artificial neural network method. Environ Monit Assess 187, 589 (2015). https://doi.org/10.1007/s10661-015-4811-0

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