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

被引：1

作者：

Bao, Tran Dinh ^{[1
,2
]}

Vu, Trong ^{[3
]}

Tue, Nguyen Tai ^{[4
,5
]}

Quy, Tran Dang ^{[4
,5
]}

Thi, Thuy Huong Ngo ^{[6
]}

Toth, Gergely ^{[7
]}

Homoki, Zsolt ^{[7
]}

Kovacs, Tibor ^{[7
]}

Duong, Van-Hao ^{[8
]}

机构：

[1] Hanoi Univ Min & Geol HUMG, Hanoi, Vietnam

[2] Hanoi Univ Min & Geol, Innovat Sustainable & Responsible Min ISRM Res Grp, Hanoi, Vietnam

[3] Quang Ninh Univ Ind, Fac Mining & Construct, Dong Trieu, Quang Ninh, Vietnam

[4] Vietnam Natl Univ, Univ Sci, VNU Key Lab Geoenvironm & Climate Change Response, Hanoi, Vietnam

[5] Vietnam Natl Univ, Univ Sci, Fac Geol, Hanoi, Vietnam

[6] Phenikaa Univ, Fac Biotechnol Chem & Environm Engn, Hanoi 12116, Vietnam

[7] Univ Pannonia, Inst Radiochem & Radioecol, Veszprem, Hungary

[8] Natl Univ, VNU Sch Interdisciplinary Studies, 144 Xuan Thuy St, Hanoi 100000, Vietnam

来源：

JOURNAL OF RADIOANALYTICAL AND NUCLEAR CHEMISTRY | 2024年 / 333卷 / 06期

关键词：

Radon prediction; Sin Quyen; Machine learning; One-hidden-layer; ANN; URANIUM-MINE; DISPERSION; RN-222; THORON; MODEL;

D O I：

10.1007/s10967-023-09281-w

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

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/m3), MABE = 2.10(%), R2 = 0.990, r = 0.99, for training part; MAPE = 1.12(%), RMSE = 2.79(Bq/m3), MABE = 2.09(%), R2 = 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.

引用

页码：3291 / 3306

页数：16

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