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- W2187767742 abstract "The main objective of the study is to define the optimal model for predicting the radiation levels of airborne radon and thoron in some Egyptian phosphate mines utilizing both statistical relationships and artificial neural network. Such prediction can be use to estimate the occupational radiation exposure of mine workers as well as for saving the time, effort and money. The study is carried out on two Egyptian phosphate mines. Radiation measurements of airborne radon and thoron have been conducted in the two mines. These measurements have been analyzed to predict the airborne radioactivity of radon and thoron levels in these mines. Six cases for predicting radon and thoron levels are investigated in each mine. Some of accuracy measurements are calculated to assess and compare the performance of statistical models and artificial neural network. The results show that using artificial neural network method for predicting both radon and thoron levels at half distance of the mine is better than the predicting each of radon or thoron separately. It is also found that the neural network method is much better than using statistical models for predicting the levels at the same distance. However, using statistical models for predicting radon or thoron levels at all distances of the mine is found to be better than using artificial neural network at half distance of the mine. The results indicated that by using two statistical models, it is not necessary to measure the levels of radon and thoron in the mine and it is possible to anticipate levels of radon and thoron all over the mine in accordance with distances. (G. I. El-Shanshoury and Eman Sarwat. Prediction of Airborne Radioactivity Levels in Mines Using Statistical Relationships and Artificial Neural Network. J Am Sci 2012;8 (9):358-370) (ISSN: 1545-1003). http://www.jofamericanscience.org . 52" @default.
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- W2187767742 date "2012-01-01" @default.
- W2187767742 modified "2023-09-27" @default.
- W2187767742 title "Prediction of Airborne Radioactivity Levels in Mines Using Statistical Relationships and Artificial Neural Network" @default.
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