A Comparative Study of Different Machine Learning Algorithms in Predicting the Content of Ilmenite in Titanium Placer
In this study, the ilmenite content in beach placer sand was estimated using seven soft computing techniques, namely random forest (RF), artificial neural network (ANN), <i>k</i>-nearest neighbors (kNN), cubist, support vector machine (SVM), stochastic gradient boosting (SGB), and classi...
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MDPI AG
2020-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/2/635 |
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| author | Yingli LV Qui-Thao Le Hoang-Bac Bui Xuan-Nam Bui Hoang Nguyen Trung Nguyen-Thoi Jie Dou Xuan Song |
| author_facet | Yingli LV Qui-Thao Le Hoang-Bac Bui Xuan-Nam Bui Hoang Nguyen Trung Nguyen-Thoi Jie Dou Xuan Song |
| author_sort | Yingli LV |
| collection | DOAJ |
| description | In this study, the ilmenite content in beach placer sand was estimated using seven soft computing techniques, namely random forest (RF), artificial neural network (ANN), <i>k</i>-nearest neighbors (kNN), cubist, support vector machine (SVM), stochastic gradient boosting (SGB), and classification and regression tree (CART). The 405 beach placer borehole samples were collected from Southern Suoi Nhum deposit, Binh Thuan province, Vietnam, to test the feasibility of these soft computing techniques in estimating ilmenite content. Heavy mineral analysis indicated that valuable minerals in the placer sand are zircon, ilmenite, leucoxene, rutile, anatase, and monazite. In this study, five materials, namely rutile, anatase, leucoxene, zircon, and monazite, were used as the input variables to estimate ilmenite content based on the above mentioned soft computing models. Of the whole dataset, 325 samples were used to build the regarded soft computing models; 80 remaining samples were used for the models’ verification. Root-mean-squared error (RMSE), determination coefficient (R<sup>2</sup>), a simple ranking method, and residuals analysis technique were used as the statistical criteria for assessing the model performances. The numerical experiments revealed that soft computing techniques are capable of estimating the content of ilmenite with high accuracy. The residuals analysis also indicated that the SGB model was the most suitable for determining the ilmenite content in the context of this research. |
| first_indexed | 2024-04-11T23:40:55Z |
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| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-11T23:40:55Z |
| publishDate | 2020-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-1da137705bcf4e4fb697a7b0ebaed0b42022-12-22T03:56:47ZengMDPI AGApplied Sciences2076-34172020-01-0110263510.3390/app10020635app10020635A Comparative Study of Different Machine Learning Algorithms in Predicting the Content of Ilmenite in Titanium PlacerYingli LV0Qui-Thao Le1Hoang-Bac Bui2Xuan-Nam Bui3Hoang Nguyen4Trung Nguyen-Thoi5Jie Dou6Xuan Song7Department of Electrical Engineering, Jiyuan Vocational and Technical College, Jiyuan 459000, ChinaDepartment of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology, 18 Vien st., Duc Thang ward, Bac Tu Liem dist., Hanoi 100000, VietnamFaculty of Geosciences and Geoengineering, Hanoi University of Mining and Geology, 18 Vien st., Duc Thang ward, Bac Tu Liem dist., Hanoi 100000, VietnamDepartment of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology, 18 Vien st., Duc Thang ward, Bac Tu Liem dist., Hanoi 100000, VietnamInstitute of Research and Development, Duy Tan University, Da Nang 550000, VietnamDivision of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City 700000, VietnamCivil and Environmental Engineering, Nagaoka University of Technology, 1603-1, Kami-Tomioka, Nagaoka, Niigata 940-2188, JapanCenter for Spatial Information Science, the University of Tokyo, 5-1-5, Kashiwa 277-8568, JapanIn this study, the ilmenite content in beach placer sand was estimated using seven soft computing techniques, namely random forest (RF), artificial neural network (ANN), <i>k</i>-nearest neighbors (kNN), cubist, support vector machine (SVM), stochastic gradient boosting (SGB), and classification and regression tree (CART). The 405 beach placer borehole samples were collected from Southern Suoi Nhum deposit, Binh Thuan province, Vietnam, to test the feasibility of these soft computing techniques in estimating ilmenite content. Heavy mineral analysis indicated that valuable minerals in the placer sand are zircon, ilmenite, leucoxene, rutile, anatase, and monazite. In this study, five materials, namely rutile, anatase, leucoxene, zircon, and monazite, were used as the input variables to estimate ilmenite content based on the above mentioned soft computing models. Of the whole dataset, 325 samples were used to build the regarded soft computing models; 80 remaining samples were used for the models’ verification. Root-mean-squared error (RMSE), determination coefficient (R<sup>2</sup>), a simple ranking method, and residuals analysis technique were used as the statistical criteria for assessing the model performances. The numerical experiments revealed that soft computing techniques are capable of estimating the content of ilmenite with high accuracy. The residuals analysis also indicated that the SGB model was the most suitable for determining the ilmenite content in the context of this research.https://www.mdpi.com/2076-3417/10/2/635titanium placerbeach placerilmenite contentartificial intelligenceapplied soft computing |
| spellingShingle | Yingli LV Qui-Thao Le Hoang-Bac Bui Xuan-Nam Bui Hoang Nguyen Trung Nguyen-Thoi Jie Dou Xuan Song A Comparative Study of Different Machine Learning Algorithms in Predicting the Content of Ilmenite in Titanium Placer Applied Sciences titanium placer beach placer ilmenite content artificial intelligence applied soft computing |
| title | A Comparative Study of Different Machine Learning Algorithms in Predicting the Content of Ilmenite in Titanium Placer |
| title_full | A Comparative Study of Different Machine Learning Algorithms in Predicting the Content of Ilmenite in Titanium Placer |
| title_fullStr | A Comparative Study of Different Machine Learning Algorithms in Predicting the Content of Ilmenite in Titanium Placer |
| title_full_unstemmed | A Comparative Study of Different Machine Learning Algorithms in Predicting the Content of Ilmenite in Titanium Placer |
| title_short | A Comparative Study of Different Machine Learning Algorithms in Predicting the Content of Ilmenite in Titanium Placer |
| title_sort | comparative study of different machine learning algorithms in predicting the content of ilmenite in titanium placer |
| topic | titanium placer beach placer ilmenite content artificial intelligence applied soft computing |
| url | https://www.mdpi.com/2076-3417/10/2/635 |
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