International Journal of the Society of Materials Engineering for Resources
Online ISSN : 1884-6629
Print ISSN : 1347-9725
ISSN-L : 1347-9725
Research articles
Applying Nonnegative Matrix Factorization for Underground Mining Method Selection Based on Mining Projects' Historical Data
Elsa Pansilvania Andre MANJATE Yoko OHTOMOTakahiko ARIMATsuyoshi ADACHIBernardo Miguel BENEYouhei KAWAMURA
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2024 Volume 26 Issue 1 Pages 1-10

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Abstract

Mining methods selection (MMS) is one of the most critical and complex decision-making tasks in mine planning. The selection of underground mining methods is considered to be the most problematic due to the complexity associated with the orebody geometry, geology, and geotechnical properties. This study integrated artificial intelligence and machine learning in the MMS process by introducing the recommendation systems  (RS) approach in MMS through the nonnegative matrix factorization (NMF) algorithm. As such, the weighted nonnegative matrix factorization (WNMF) algorithm is applied to build a model for underground MMS. The study's input dataset is based on thirty mining projects' historical data. In the experiments, we evaluate the capability of the WNMF to predict underground mining methods using five input variables: ore strength, host-rock strength, orebody thickness, shape, and dip. The results show that the WNMF model achieved an average prediction accuracy of 67.5%, considered reasonable and realistic. Further findings reveal that the WNMF model is sensitive to the imbalanced class dataset used in the experiments, thus, suggesting the need to improve the dataset's quality. These results reveal the model's effectiveness in predicting underground mining methods; therefore, with continuous improvement, the WNMF model can be effectively applied in underground MMS.

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© 2023 The Society of Materials Engineering for Resources of Japan
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