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Estimating coal reserves using a support vector machine

Authors
Journal
Journal of China University of Mining and Technology
1006-1266
Publisher
Elsevier
Publication Date
Volume
18
Issue
1
Identifiers
DOI: 10.1016/s1006-1266(08)60022-x
Keywords
  • Support Vector Machine
  • Statistical Learning Theory
  • Coal Reserve

Abstract

Abstract The basic principles of the Support Vector Machine (SVM) are introduced in this paper. A specific process to establish an SVM prediction model is given. To improve the precision of coal reserve estimation, a support vector machine method, based on statistical learning theory, is put forward. The SVM model was trained and tested by using the existing exploration and exploitation data of Chencun mine of Yima bureau's as the input data. Then coal reserves within a particular region were calculated. These calculated results and the actual results of the exploration block were compared. The maximum relative error was 10.85%, within the scope of acceptable error limits. The results show that the SVM coal reserve calculation method is reliable. This method is simple, practical and valuable.

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