predicting gabal gattar uranium content as a function of total gamma-ray and thorium contents using an artificial neural network in northeastern desert, 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.