Development of Artificial Neural Network for prediction of radon dispersion released from Sinquyen Mine, Vietnam. (1st August 2021)

• Record Type: Journal Article
• Authors: Duong, Van-Hao; Ly, Hai-Bang; Trinh, Dinh Huan; Nguyen, Thai Son; Pham, Binh Thai
• Is Part Of: Environmental pollution. Volume 282(2021)
• Publication Date: 2021-08-01
• Subjects: Radon distribution -- Radon prediction -- ANN -- Machine learning -- Radon dispersion -- Optimization -- Sinquen mine; Pollution -- Periodicals; Pollution -- Environmental aspects -- Periodicals; Environmental Pollution -- Periodicals; Pollution -- Périodiques; Pollution -- Aspect de l'environnement -- Périodiques; Pollution -- Effets physiologiques -- Périodiques; Pollution; Pollution -- Environmental aspects; Periodicals; Electronic journals; 363.73
• DOI: 10.1016/j.envpol.2021.116973
• Languages: English
• ISSNs: 0269-7491

Abstract

Abstract: 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. Graphical abstract: Image 1 Highlights: Understanding the radon dispersion released from the mine is an important problem. Artificial Neural Network (ANN) is proposed to predict the radon dispersion. Structure of ANN was also optimized the get the best prediction capacity. This study will be helpful in quick and accurate prediction of radon dispersion.