Development of Artificial Neural Network for prediction of radon dispersion released from Sinquyen Mine, Vietnam
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 pre...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Duong, Van-Hao [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Structural failure performance of the encased functionally graded porous cylinder consolidated by graphene platelet under uniform radial loading - Li, Zhaochao ELSEVIER, 2019, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:282 ; year:2021 ; day:1 ; month:08 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.envpol.2021.116973 |
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ELV054245761 |
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| 245 | 1 | 0 | |a Development of Artificial Neural Network for prediction of radon dispersion released from Sinquyen Mine, Vietnam |
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| 520 | |a 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 (R2), 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., R2 = 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. | ||
| 520 | |a 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 (R2), 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., R2 = 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. | ||
| 650 | 7 | |a ANN |2 Elsevier | |
| 650 | 7 | |a Radon dispersion |2 Elsevier | |
| 650 | 7 | |a Machine learning |2 Elsevier | |
| 650 | 7 | |a Radon prediction |2 Elsevier | |
| 650 | 7 | |a Radon distribution |2 Elsevier | |
| 650 | 7 | |a Sinquen mine |2 Elsevier | |
| 650 | 7 | |a Optimization |2 Elsevier | |
| 700 | 1 | |a Ly, Hai-Bang |4 oth | |
| 700 | 1 | |a Trinh, Dinh Huan |4 oth | |
| 700 | 1 | |a Nguyen, Thai Son |4 oth | |
| 700 | 1 | |a Pham, Binh Thai |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier Science |a Li, Zhaochao ELSEVIER |t Structural failure performance of the encased functionally graded porous cylinder consolidated by graphene platelet under uniform radial loading |d 2019 |g Amsterdam [u.a.] |w (DE-627)ELV00327988X |
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10.1016/j.envpol.2021.116973 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001411.pica (DE-627)ELV054245761 (ELSEVIER)S0269-7491(21)00555-8 DE-627 ger DE-627 rakwb eng 690 VZ 50.31 bkl 56.11 bkl Duong, Van-Hao verfasserin aut Development of Artificial Neural Network for prediction of radon dispersion released from Sinquyen Mine, Vietnam 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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 (R2), 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., R2 = 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. 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 (R2), 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., R2 = 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. ANN Elsevier Radon dispersion Elsevier Machine learning Elsevier Radon prediction Elsevier Radon distribution Elsevier Sinquen mine Elsevier Optimization Elsevier Ly, Hai-Bang oth Trinh, Dinh Huan oth Nguyen, Thai Son oth Pham, Binh Thai oth Enthalten in Elsevier Science Li, Zhaochao ELSEVIER Structural failure performance of the encased functionally graded porous cylinder consolidated by graphene platelet under uniform radial loading 2019 Amsterdam [u.a.] (DE-627)ELV00327988X volume:282 year:2021 day:1 month:08 pages:0 https://doi.org/10.1016/j.envpol.2021.116973 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 50.31 Technische Mechanik VZ 56.11 Baukonstruktion VZ AR 282 2021 1 0801 0 |
| spelling |
10.1016/j.envpol.2021.116973 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001411.pica (DE-627)ELV054245761 (ELSEVIER)S0269-7491(21)00555-8 DE-627 ger DE-627 rakwb eng 690 VZ 50.31 bkl 56.11 bkl Duong, Van-Hao verfasserin aut Development of Artificial Neural Network for prediction of radon dispersion released from Sinquyen Mine, Vietnam 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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 (R2), 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., R2 = 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. 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 (R2), 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., R2 = 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. ANN Elsevier Radon dispersion Elsevier Machine learning Elsevier Radon prediction Elsevier Radon distribution Elsevier Sinquen mine Elsevier Optimization Elsevier Ly, Hai-Bang oth Trinh, Dinh Huan oth Nguyen, Thai Son oth Pham, Binh Thai oth Enthalten in Elsevier Science Li, Zhaochao ELSEVIER Structural failure performance of the encased functionally graded porous cylinder consolidated by graphene platelet under uniform radial loading 2019 Amsterdam [u.a.] (DE-627)ELV00327988X volume:282 year:2021 day:1 month:08 pages:0 https://doi.org/10.1016/j.envpol.2021.116973 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 50.31 Technische Mechanik VZ 56.11 Baukonstruktion VZ AR 282 2021 1 0801 0 |
| allfields_unstemmed |
10.1016/j.envpol.2021.116973 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001411.pica (DE-627)ELV054245761 (ELSEVIER)S0269-7491(21)00555-8 DE-627 ger DE-627 rakwb eng 690 VZ 50.31 bkl 56.11 bkl Duong, Van-Hao verfasserin aut Development of Artificial Neural Network for prediction of radon dispersion released from Sinquyen Mine, Vietnam 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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 (R2), 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., R2 = 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. 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 (R2), 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., R2 = 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. ANN Elsevier Radon dispersion Elsevier Machine learning Elsevier Radon prediction Elsevier Radon distribution Elsevier Sinquen mine Elsevier Optimization Elsevier Ly, Hai-Bang oth Trinh, Dinh Huan oth Nguyen, Thai Son oth Pham, Binh Thai oth Enthalten in Elsevier Science Li, Zhaochao ELSEVIER Structural failure performance of the encased functionally graded porous cylinder consolidated by graphene platelet under uniform radial loading 2019 Amsterdam [u.a.] (DE-627)ELV00327988X volume:282 year:2021 day:1 month:08 pages:0 https://doi.org/10.1016/j.envpol.2021.116973 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 50.31 Technische Mechanik VZ 56.11 Baukonstruktion VZ AR 282 2021 1 0801 0 |
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10.1016/j.envpol.2021.116973 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001411.pica (DE-627)ELV054245761 (ELSEVIER)S0269-7491(21)00555-8 DE-627 ger DE-627 rakwb eng 690 VZ 50.31 bkl 56.11 bkl Duong, Van-Hao verfasserin aut Development of Artificial Neural Network for prediction of radon dispersion released from Sinquyen Mine, Vietnam 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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 (R2), 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., R2 = 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. 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 (R2), 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., R2 = 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. ANN Elsevier Radon dispersion Elsevier Machine learning Elsevier Radon prediction Elsevier Radon distribution Elsevier Sinquen mine Elsevier Optimization Elsevier Ly, Hai-Bang oth Trinh, Dinh Huan oth Nguyen, Thai Son oth Pham, Binh Thai oth Enthalten in Elsevier Science Li, Zhaochao ELSEVIER Structural failure performance of the encased functionally graded porous cylinder consolidated by graphene platelet under uniform radial loading 2019 Amsterdam [u.a.] (DE-627)ELV00327988X volume:282 year:2021 day:1 month:08 pages:0 https://doi.org/10.1016/j.envpol.2021.116973 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 50.31 Technische Mechanik VZ 56.11 Baukonstruktion VZ AR 282 2021 1 0801 0 |
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10.1016/j.envpol.2021.116973 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001411.pica (DE-627)ELV054245761 (ELSEVIER)S0269-7491(21)00555-8 DE-627 ger DE-627 rakwb eng 690 VZ 50.31 bkl 56.11 bkl Duong, Van-Hao verfasserin aut Development of Artificial Neural Network for prediction of radon dispersion released from Sinquyen Mine, Vietnam 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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 (R2), 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., R2 = 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. 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 (R2), 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., R2 = 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. ANN Elsevier Radon dispersion Elsevier Machine learning Elsevier Radon prediction Elsevier Radon distribution Elsevier Sinquen mine Elsevier Optimization Elsevier Ly, Hai-Bang oth Trinh, Dinh Huan oth Nguyen, Thai Son oth Pham, Binh Thai oth Enthalten in Elsevier Science Li, Zhaochao ELSEVIER Structural failure performance of the encased functionally graded porous cylinder consolidated by graphene platelet under uniform radial loading 2019 Amsterdam [u.a.] (DE-627)ELV00327988X volume:282 year:2021 day:1 month:08 pages:0 https://doi.org/10.1016/j.envpol.2021.116973 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 50.31 Technische Mechanik VZ 56.11 Baukonstruktion VZ AR 282 2021 1 0801 0 |
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Development of Artificial Neural Network for prediction of radon dispersion released from Sinquyen Mine, Vietnam |
| 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 (R2), 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., R2 = 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. |
| abstractGer |
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 (R2), 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., R2 = 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. |
| abstract_unstemmed |
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 (R2), 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., R2 = 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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