Predicting Gabal Gattar Uranium Content as a Function of Total Gamma-ray and Thorium Contents using an Artificial Neural Network in Northeastern Desert, Egypt
- سال انتشار: 1403
- محل انتشار: مجله معدن و محیط زیست، دوره: 15، شماره: 1
- کد COI اختصاصی: JR_JMAE-15-1_009
- زبان مقاله: انگلیسی
- تعداد مشاهده: 53
نویسندگان
Mining and Petroleum Engineering Department, Faculty of Engineering, Al-Azhar University, Cairo, Egypt.
Mining and Petroleum Engineering Department, Faculty of Engineering, Al-Azhar University, Cairo, Egypt.
Mining and Petroleum Engineering Department, Faculty of Engineering, Al-Azhar University, Cairo, Egypt.
Mining and Petroleum Engineering Department, Faculty of Engineering, Al-Azhar University, Cairo, Egypt.
چکیده
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 ۱۲۲۱ 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 (R۲) of about ۰.۹۹۹, a Root Mean Square Error (RMSE) equal to ۰.۱۱۵%, a Mean Relative Error (MRE) of -۰.۰۵%, and a Mean Absolute Relative Error (MARE) of ۰.۷۶%. Comparing the obtained results with the field investigation shows that the suggested ANN model performed well.کلیدواژه ها
ANN, Uranium and Thorium concentrations, Total Gamma-ray, Modelling, Gattar areaاطلاعات بیشتر در مورد COI
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