Document Type : Review Paper


1 Associate professor, Al-Azhar University, Faculty of Engineering Mining and Petroleum Engineering Department

2 Faculty of Engineering, Al-Azhar University, Cairo, Egypt

3 Associate Lecture at Nuclear Materials Authority, Cairo, Egypt.

4 Professor at the Department of Mining and Petroleum Engineering, Al-Azhar University, Cairo, Egypt



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.


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