Development of Artificial Neural Network for prediction of radon dispersion released from Sinquyen Mine, Vietnam.

Autor: Duong VH; Hanoi University of Mining and Geology, 18 Vien Street, Bac Tu Liem District, Hanoi, Viet Nam. Electronic address: duongvanhao@humg.edu.vn., Ly HB; University of Transport Technology, Hanoi, 100000, Viet Nam. Electronic address: banglh@utt.edu.vn., Trinh DH; Radioactive & Rare Minerals Division, Xuan Phuong, Bac Tu Liem, Hanoi, Viet Nam. Electronic address: huan.trinhdinh@gmail.com., Nguyen TS; Hanoi University of Mining and Geology, 18 Vien Street, Bac Tu Liem District, Hanoi, Viet Nam; Radioactive & Rare Minerals Division, Xuan Phuong, Bac Tu Liem, Hanoi, Viet Nam. Electronic address: nguyenthaisondvl@gmail.com., Pham BT; University of Transport Technology, Hanoi, 100000, Viet Nam. Electronic address: binhpt@utt.edu.vn.
Jazyk: angličtina
Zdroj: Environmental pollution (Barking, Essex : 1987) [Environ Pollut] 2021 Aug 01; Vol. 282, pp. 116973. Date of Electronic Publication: 2021 Mar 23.
DOI: 10.1016/j.envpol.2021.116973
Abstrakt: Understanding the radon dispersion released from this mine are important targets as radon dispersion is used to assess radiological hazard to human. In this paper, the main objective is to develop and optimize a machine learning model namely Artificial Neural Network (ANN) for quick and accurate prediction of radon dispersion released from Sinquyen mine, Vietnam. For this purpose, a total of million data collected from the study area, which includes input variables (the gamma data of uranium concentration with 3 × 3m grid net survey inside mine, 21 of CR-39 detectors inside dwellings surrounding mine, and gamma dose at 1 m from ground surface data) and an output variable (radon dispersion) were used for training and validating the predictive model. Various validation methods namely coefficient of determination (R 2 ), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) were used. In addition, Partial dependence plots (PDP) was used to evaluate the effect of each input variable on the predictive results of output variable. The results show that ANN performed well for prediction of radon dispersion, with low values of error (i.e., R 2  = 0.9415, RMSE = 0.0589, and MAE = 0.0203 for the testing dataset). The increase of number of hidden layers in ANN structure leads the increase of accuracy of the predictive results. The sensitivity results show that all input variables govern the dispersion radon activity with different amplitudes and fitted with different equations but the gamma dose is the most influenced and important variable in comparison with strike, distance and uranium concentration variables for prediction of radon dispersion.
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Databáze: MEDLINE
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