Machine learning application for radon release prediction from the copper ore mining in Sin Quyen, Lao Cai, North Vietnam

The radon release prediction from radioactive-bearing mines during mineral processing and mining is an essential target. A simple one-hidden-layer artificial neural network (ANN) model was designed with low computation cost to train, reference and get optimum effectiveness in comparison with two-hid...

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Bibliographic Details
Published in:Journal of radioanalytical and nuclear chemistry 2024, Vol.333 (6), p.3291-3306
Main Authors: Dinh Bao, Tran, Vu, Trong, Tue, Nguyen Tai, Quy, Tran Dang, Ngo Thi, Thuy Huong, Toth, Gergely, Homoki, Zsolt, Kovacs, Tibor, Duong, Van-Hao
Format: Article
Language:English
Subjects:
Artificial neural networks
Chemistry
Chemistry and Materials Science
Copper ores
Diagnostic Radiology
Hadrons
Heavy Ions
Inorganic Chemistry
Machine learning
Mineral processing
Nuclear Chemistry
Nuclear Physics
Physical Chemistry
Radon
Support vector machines
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Summary:The radon release prediction from radioactive-bearing mines during mineral processing and mining is an essential target. A simple one-hidden-layer artificial neural network (ANN) model was designed with low computation cost to train, reference and get optimum effectiveness in comparison with two-hidden-layer ANN, random forest and support vector machine models which was applied for Sin Quyen copper deposit. The result showed with values of MAPE  = 1.12(%), RMSE = 2.79(Bq/m 3 ), MABE  = 2.10(%), R 2  = 0.990, r  = 0.99, for training part; MAPE  = 1.12(%), RMSE  = 2.79(Bq/m 3 ), MABE  = 2.09(%), R 2  = 0.995, r  = 0.997 for testing part. The gamma dose and distance were significantly more effective variables for the radon prediction than direction, coordinate, and uranium concentration factors.
ISSN:0236-5731
1588-2780
DOI:10.1007/s10967-023-09281-w