Pages: 226-232

Authors: Aleksandra D. Samolov, Snežana D. Dragović, Marko Ž. Daković
and Goran G. Bačić

Abstract

The application of the principal component analysis and artificial neural network method in forecasting ¹³⁷Cs behaviour in the air as the function of meteorological parameters is presented. The model was optimized and tested using ¹³⁷Cs specific activities obtained by standard gamma-ray spectrometric analysis of air samples collected in Belgrade (Serbia) during 2009-2011 and meteorological data for the same period. Low correlation (r = 0.20) between experimental values of ¹³⁷Cs specific activities and those predicted by artificial neural network was obtained. This suggests that artificial neural network in the case of prediction of ¹³⁷Cs specific activity, using temperature, insolation, and global Sun warming does not perform well, which can be explained by the relative independence of ¹³⁷Cs specific activity of particular meteorological parameters and not by the ineffectiveness of artificial neural network in relating these parameters in general.

Key words: neural network, gamma-ray spectrometry, air, ¹³⁷Cs

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