Forecasting of Ra(226), Th(232) and U(238) Concentrations using Artificial Neural Networks (ANNs)
Abstract
Identification and modeling of radioactive
concentrations in a region is very important for the region in terms of
radiological hazards. Artificial Neural Network (ANN) can successfully model
large systems. The validity of the
model was tested by entering the data of the proposed ANN model that had never
been entered into the system. In this research, average activity
concentrations of 226Ra, 232Th
and 238U in the water samples collected from the lake are 1.439 Bql-1, 4.508 Bql-1 and
14.682 Bql-1, respectively. The
characteristics of the study area are also determined with the spatial maps and
ANNs are used to prediction and modeling of the radionuclides. The mean square
errors for the obtained results are less than 1.5%. The correlation coefficient close to +1 indicates
the validity of the model for this study.
Keywords
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