An AI-Based Approach for Developing a Recommendation System for Underground Mining Methods Pre-Selection
Selecting the most appropriate mining method to recover mineral resources is a critical decision-making task in mining project development. This study introduces an artificial intelligence-based mining methods recommendation system (AI-MMRS) for the pre-selection of underground mining methods. The s...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-10-01
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| Series: | Mining |
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| Online Access: | https://www.mdpi.com/2673-6489/4/4/42 |
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| author | Elsa Pansilvania Andre Manjate Natsuo Okada Yoko Ohtomo Tsuyoshi Adachi Bernardo Miguel Bene Takahiko Arima Youhei Kawamura |
| author_facet | Elsa Pansilvania Andre Manjate Natsuo Okada Yoko Ohtomo Tsuyoshi Adachi Bernardo Miguel Bene Takahiko Arima Youhei Kawamura |
| author_sort | Elsa Pansilvania Andre Manjate |
| collection | DOAJ |
| description | Selecting the most appropriate mining method to recover mineral resources is a critical decision-making task in mining project development. This study introduces an artificial intelligence-based mining methods recommendation system (AI-MMRS) for the pre-selection of underground mining methods. The study integrates and evaluates the capability of two approaches for mining methods selection (MMS): the memory-based collaborative filtering (CF) approach aided by the UBC-MMS system to predict the top-3 relevant mining methods and supervised machine learning (ML) classification algorithms to enhance the effectiveness and novelty of the AI-MMRS, addressing the limitations of the CF approach. The results reveal that the memory-based CF approach achieves an accuracy ranging from 81.8% to 87.9%. Among the classification algorithms, artificial neural network (ANN) and k-nearest neighbors (KNN) classifiers perform the best, with accuracy levels of 66.7% and 63.6%, respectively. These findings demonstrate the effectiveness and viability of both approaches in MMS, acknowledging their limitations and the need for continuous training and optimization. The proposed AI-MMRS for the pre-selection stage supplemented by the direct involvement of mining professionals in later stages of MMS, has the potential to significantly aid in the MMS decision-making, providing data-driven and experience-based recommendations following the ongoing evolution of mining practices. |
| format | Article |
| id | doaj-art-1c8702d4907d41cfa8fd5ae42d5b572f |
| institution | DOAJ |
| issn | 2673-6489 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mining |
| spelling | doaj-art-1c8702d4907d41cfa8fd5ae42d5b572f2025-08-20T02:56:59ZengMDPI AGMining2673-64892024-10-014474776510.3390/mining4040042An AI-Based Approach for Developing a Recommendation System for Underground Mining Methods Pre-SelectionElsa Pansilvania Andre Manjate0Natsuo Okada1Yoko Ohtomo2Tsuyoshi Adachi3Bernardo Miguel Bene4Takahiko Arima5Youhei Kawamura6Division of Sustainable Resources Engineering, Graduate School of Engineering, Hokkaido University, Sapporo 060-8628, JapanDivision of Sustainable Resources Engineering, Graduate School of Engineering, Hokkaido University, Sapporo 060-8628, JapanDivision of Sustainable Resources Engineering, Faculty of Engineering, Hokkaido University, Sapporo 060-8628, JapanGraduate School of International Resource Sciences, Akita University, Akita 010-8502, JapanInstituto Superior Politécnico de Tete, Tete 2300, MozambiqueDivision of Sustainable Resources Engineering, Faculty of Engineering, Hokkaido University, Sapporo 060-8628, JapanDivision of Sustainable Resources Engineering, Faculty of Engineering, Hokkaido University, Sapporo 060-8628, JapanSelecting the most appropriate mining method to recover mineral resources is a critical decision-making task in mining project development. This study introduces an artificial intelligence-based mining methods recommendation system (AI-MMRS) for the pre-selection of underground mining methods. The study integrates and evaluates the capability of two approaches for mining methods selection (MMS): the memory-based collaborative filtering (CF) approach aided by the UBC-MMS system to predict the top-3 relevant mining methods and supervised machine learning (ML) classification algorithms to enhance the effectiveness and novelty of the AI-MMRS, addressing the limitations of the CF approach. The results reveal that the memory-based CF approach achieves an accuracy ranging from 81.8% to 87.9%. Among the classification algorithms, artificial neural network (ANN) and k-nearest neighbors (KNN) classifiers perform the best, with accuracy levels of 66.7% and 63.6%, respectively. These findings demonstrate the effectiveness and viability of both approaches in MMS, acknowledging their limitations and the need for continuous training and optimization. The proposed AI-MMRS for the pre-selection stage supplemented by the direct involvement of mining professionals in later stages of MMS, has the potential to significantly aid in the MMS decision-making, providing data-driven and experience-based recommendations following the ongoing evolution of mining practices.https://www.mdpi.com/2673-6489/4/4/42mining methods selectionunderground miningdecision-makingrecommendation systemmemory-based collaborative filteringclassification machine learning |
| spellingShingle | Elsa Pansilvania Andre Manjate Natsuo Okada Yoko Ohtomo Tsuyoshi Adachi Bernardo Miguel Bene Takahiko Arima Youhei Kawamura An AI-Based Approach for Developing a Recommendation System for Underground Mining Methods Pre-Selection Mining mining methods selection underground mining decision-making recommendation system memory-based collaborative filtering classification machine learning |
| title | An AI-Based Approach for Developing a Recommendation System for Underground Mining Methods Pre-Selection |
| title_full | An AI-Based Approach for Developing a Recommendation System for Underground Mining Methods Pre-Selection |
| title_fullStr | An AI-Based Approach for Developing a Recommendation System for Underground Mining Methods Pre-Selection |
| title_full_unstemmed | An AI-Based Approach for Developing a Recommendation System for Underground Mining Methods Pre-Selection |
| title_short | An AI-Based Approach for Developing a Recommendation System for Underground Mining Methods Pre-Selection |
| title_sort | ai based approach for developing a recommendation system for underground mining methods pre selection |
| topic | mining methods selection underground mining decision-making recommendation system memory-based collaborative filtering classification machine learning |
| url | https://www.mdpi.com/2673-6489/4/4/42 |
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