Mostafaei, Kamran, Maleki, Shahoo, Jodeiri Shokri, Behshad, Yousefi, Mahyar. (1402). Predicting gold grade by using support vector machine and neural network to generate an evidence layer for 3D prospectivity analysis. , 57(4), 435-444. doi: 10.22059/ijmge.2023.362951.595087
Kamran Mostafaei; Shahoo Maleki; Behshad Jodeiri Shokri; Mahyar Yousefi. "Predicting gold grade by using support vector machine and neural network to generate an evidence layer for 3D prospectivity analysis". , 57, 4, 1402, 435-444. doi: 10.22059/ijmge.2023.362951.595087
Mostafaei, Kamran, Maleki, Shahoo, Jodeiri Shokri, Behshad, Yousefi, Mahyar. (1402). 'Predicting gold grade by using support vector machine and neural network to generate an evidence layer for 3D prospectivity analysis', , 57(4), pp. 435-444. doi: 10.22059/ijmge.2023.362951.595087
Mostafaei, Kamran, Maleki, Shahoo, Jodeiri Shokri, Behshad, Yousefi, Mahyar. Predicting gold grade by using support vector machine and neural network to generate an evidence layer for 3D prospectivity analysis. , 1402; 57(4): 435-444. doi: 10.22059/ijmge.2023.362951.595087

Predicting gold grade by using support vector machine and neural network to generate an evidence layer for 3D prospectivity analysis

International Journal of Mining and Geo-Engineering
مقاله 10، دوره 57، شماره 4، زمستان 2023، صفحه 435-444    اصل مقاله (972.51 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/ijmge.2023.362951.595087
نویسندگان
Kamran Mostafaei* 1؛ Shahoo Maleki2؛ Behshad Jodeiri Shokri3؛ Mahyar Yousefi4
1Department of Mining, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran.
2Faculty of Mining Engineering, Amirkabir University of Technology, Tehran, Iran.
3School of Engineering, University of Southern Queensland, Springfield Campus, Springfield, Australia.
4Associate professor, Faculty of Engineering, Malayer University, Malayer, Iran .
چکیده
This paper uses support vector machine (SVM), back propagation neural network (BPNN), and Multivariate Regression Analysis (MLA) methods to predict the gold in the Dalli deposit situated in the central province of Iran. After analyzing the data, the dataset was prepared. Subsequently, through comprehensive statistical analyses, Au was chosen as the output element for modelling, while Cu, Al, Ca, Fe, Ti, and Zn were considered 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, while the remaining data were assigned for the testing stage. The correlation coefficients for SVM, BPNN, and MLA were 94%, 75%, and 68%, respectively. A comparison of these coefficients revealed that all used methods successfully predicted the actual grade of Au. However, the SVM was more reliable and accurate than other methods. Considering the sensitivity of the gold data and the small number in the exploratory database, the results of this research are used to prepare the main layer in the mineral prospectivity mapping (MPM) of gold in 2 and 3D.
کلیدواژه‌ها
Gold grade estimation؛ Support vector machine؛ Back propagation neural network؛ Dalli deposit, Iran
مراجع
آمار
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