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چکیده
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This paper uses a support vector machine (SVM) and back propagation neural network (BPNN) methods to predict the gold in the Dalli deposit in the central province of Iran. Based on the results of the data analysis, the dataset was prepared. Then according to comprehensive statistical analyses, Au was chosen as an output element modeling, while Cu, Al, Ca, Fe, Ti, and Zn were input parameters. Then, the dataset was divided into two groups training and testing datasets. For this purpose, 70% of the datasets were randomly entered into the data process, and the rest of the data were assigned to the test of the procedure. The correlation coefficients for SVM and BPNN were 94% and 75%, respectively. A comparison of the correlation coefficients revealed that both methods of SVM and BPNN could successfully predict the actual grade of Au. However, SVM was more reliable and more accurate than BPNNhod.
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