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Journal of Mining and Environment، جلد ۱۵، شماره ۱، صفحات ۱۷۵-۱۸۹
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عنوان انگلیسی Predicting Gabal Gattar Uranium Content as a Function of Total Gamma-ray and Thorium Contents using an Artificial Neural Network in Northeastern Desert, Egypt
چکیده انگلیسی مقاله This study aims to develop an empirical correlation model for estimating the uranium content of the G-V in the Gabal Gattar area, northeastern desert of Egypt, as a function of the thorium content and the total gamma rays. Using the recent MATLAB software, the effect of selecting tan-sigmoid as a transfer function at various numbers of hidden neurons was investigated to arrive at the optimum Artificial Neural Network (ANN) model. The pure-linear function was investigated as the output function, and the Levenberg-Marquardt approach was chosen as the optimization technique. Based on 1221 datasets, a novel ANN-based empirical correlation was developed to calculate the amounts of uranium (U). The results show a wide range of uranium content, with a determination coefficient (R2) of about 0.999, a Root Mean Square Error (RMSE) equal to 0.115%, a Mean Relative Error (MRE) of -0.05%, and a Mean Absolute Relative Error (MARE) of 0.76%. Comparing the obtained results with the field investigation shows that the suggested ANN model performed well.
کلیدواژه‌های انگلیسی مقاله ANN, Uranium and Thorium concentrations, Total Gamma-ray, Modelling, Gattar area
نویسندگان مقاله Abdelrahem Khalefa Embaby |
Mining and Petroleum Engineering Department, Faculty of Engineering, Al-Azhar University, Cairo, Egypt.

Sayed Gomaa |
Mining and Petroleum Engineering Department, Faculty of Engineering, Al-Azhar University, Cairo, Egypt.

Yehia Darwish |
Mining and Petroleum Engineering Department, Faculty of Engineering, Al-Azhar University, Cairo, Egypt.

Samir Selim |
Mining and Petroleum Engineering Department, Faculty of Engineering, Al-Azhar University, Cairo, Egypt.

نشانی اینترنتی https://jme.shahroodut.ac.ir/article_2952_58c0c76cb76345be2f3a14a228aa5178.pdf
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