공간보간법 기반 선량평가 시 측정값 분포 및 개수별 평가 결과 분석
Analysis of Dose Assessment Results Based on Spatial Interpolation by Distribution and Number of Measurements
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
In the emergency situation at nuclear power plant, the radiation dose levels of emergency workersshould be analyzed to optimize their radiation levels. However, analyzing the dose level of workers can be difficultdue to the lack of dose rate information in some workspaces. In such cases, spatial interpolation can be used to quicklyperform a dose assessment using only relatively easy-to-obtain measurement data. However, spatial interpolation canproduce different interpolation results depending on the distribution and number of the measurements. The purposeof this study is to analyze the evaluation results of spatial interpolation method by the distribution and number ofmeasurement data. For this purpose, K-NN, IDW, and Kriging interpolation methods were analyzed, and a simulatedworkspace and distribution and number of the measurement data for dose assessment were set up. For the distributionof the measurement data, three situations were set: 1) distribution over the entire workspace, 2) distribution in thehigh dose rate area, and 3) distribution in the low dose rate area. The number of measurements was set to 8, 35, and96. The radiation dose rates were evaluated by the distribution and number of the measurement data for each spatialinterpolation method and the results were compared with the evaluation results of the MCNP computer code. Theresults showed that when measurements are distributed over the entire workspace, K-NN method showed the lowesterror rate within 5.00% for 35 and 96 measurements. For 8 measurements, Kriging method showed lowest error rate,but it was similar to K-NN. When measurements are distributed in the high dose rate area, Kriging method showedan error rate of more than 100% when there were 8 measurements, but as the number of measurements increased,the error rate decreased to between 16.2% and 30.4%, the lowest error rate of the three interpolation methods. When measurements are distributed in the low dose rate area, Kriging interpolation showed a relatively high errorrate compared to the other two interpolation methods when there were 8 measurements. However, as the numberof measurements increased, the error rate decreased sharply and was less than 5.00%, the lowest error rate of thethree interpolation methods. The results of this study are expected to be used as a basis for emergency worker doseassessment based on the spatial interpolation method.
Abstract
In the emergency situation at nuclear power plant, the radiation dose levels of emergency workersshould be analyzed to optimize their radiation levels. However, analyzing the dose level of workers can be difficultdue to the lack of dose rate information in some workspaces. In such cases, spatial interpolation can be used to quicklyperform a dose assessment using only relatively easy-to-obtain measurement data. However, spatial interpolation canproduce different interpolation results depending on the distribution and number of the measurements. The purposeof this study is to analyze the evaluation results of spatial interpolation method by the distribution and number ofmeasurement data. For this purpose, K-NN, IDW, and Kriging interpolation methods were analyzed, and a simulatedworkspace and distribution and number of the measurement data for dose assessment were set up. For the distributionof the measurement data, three situations were set: 1) distribution over the entire workspace, 2) distribution in thehigh dose rate area, and 3) distribution in the low dose rate area. The number of measurements was set to 8, 35, and96. The radiation dose rates were evaluated by the distribution and number of the measurement data for each spatialinterpolation method and the results were compared with the evaluation results of the MCNP computer code. Theresults showed that when measurements are distributed over the entire workspace, K-NN method showed the lowesterror rate within 5.00% for 35 and 96 measurements. For 8 measurements, Kriging method showed lowest error rate,but it was similar to K-NN. When measurements are distributed in the high dose rate area, Kriging method showedan error rate of more than 100% when there were 8 measurements, but as the number of measurements increased,the error rate decreased to between 16.2% and 30.4%, the lowest error rate of the three interpolation methods. When measurements are distributed in the low dose rate area, Kriging interpolation showed a relatively high errorrate compared to the other two interpolation methods when there were 8 measurements. However, as the numberof measurements increased, the error rate decreased sharply and was less than 5.00%, the lowest error rate of thethree interpolation methods. The results of this study are expected to be used as a basis for emergency worker doseassessment based on the spatial interpolation method.
참고문헌 (21)
- Application of the Conmmision`s Recommendations for the Protection of People in Emergency Exposure Situations, "ICRP", ICRP publication, ~ (2009)
- Radiological Safety and Control, "KAERI", ~, pp.KAERI (1996), TR-618[DOI]
- Wang Zhenyu, "Surrogate Model of solved poisson kriging method for radiation field reconstruction", Nucl. Eng. Thechnol (2024)[DOI]
- Bleher M, "Reduction of uncertainties in exposure assessment based on environmental monitoring data", Radioprotection, 55(HS1), pp.S81~S88 (2020)[DOI]
- Li J, "Spatial Interpolation Methods Applied in the Environmental Sciences : A Review", Environ. Modell. Softw, 53, pp.173~189 (2014)[DOI]
- Comber A, "Spatial Interpolation Using Areal Features : A Review of Methods and Opportunites using new forms of data with coded illustrations", Geogr. Compass, 13(10), pp.1~23 (2019)[DOI]
- West A, "Use of Gaussian process regression for radiation mapping of a nuclear reactor with a mobile robot", Nature, 11(1), pp.1~11 (2021)[DOI]
- Khuwaileh BA, "Gaussian Process Approach for Dose Mapping in Radiation Fields", Nucl. Eng. Thechnol, 52(8), pp.1807~1816 (2020)[DOI]
- Harun N, "Spatial Interpolation of Gamma Dose in Radioactive Waste Storage Facility", IOP conf. Ser.:Mater. Sci. Eng (2018)
- Yesilkanat CM, "Dose Rate Estimates and Spatial Interpolation Maps of Outdoor Gamma Dose Rate with Geostatistical Methods", J. Environ. Radioact, 150, pp.132~144 (2015)[DOI]
- Takahiro O, "Comparison of Spatial Interpolation Methods based on Exposure Assessments of Air Pollutants: A Case Study on Nuclear Substances in Fukushima", 12th International Joint Conference on Biomedical Engineering Systems and Technologies, pp.415~421 (2019)
- Zhang H, "Spatial Sampling Strategies for the Effect of Interpolation Accuracy", ISPRS Int. J. Geo-Inf, 4(4), pp.2742~2768 (2015)[DOI]
- Jang H, "Applicability of Spatial Interpolation Methods for the Estimation of Rainfall Field", Journal of Wetlands Research, 17(4), pp.370~379 (2015)[DOI]
- Johnston K, "Arc-GIS: Using ArcGIS Geostatistical Analyst", pp.53~ (2003), ESRI press
- Radioactive Effluents from Nuclear Power Plants: Annual Report 2019, "NRC", ~, pp.NUREG (2019), CR-2907
- Yesilkanat CM, "Spatial Interpolation and Radiological mapping of Ambient Gamma Dose Rate by Using Neural Networks and Fuzzy Logic Methods", J. Environ. Radioact, 175-176, pp.78~93 (2017)[DOI]
- Peleg O, "Interpolation of Microbiome Composition in Longitudinal Date Sets", mBio, 15(9), pp.e00150~24 (2024)[DOI]
- Zhang B, "Reconstruction of γ Dose Rate Field and Algorithm Validation Based on Inverse Distance Weight Interpolation", Nucl. Technol (2024)[DOI]
- Diggle PJ, "Model-based Geostatistics", pp.100~102 (2006), Springer
- Woo KS, "Sensitivity Analysis of Ordinary Kriging Interpolation According to Different Variogram Models", J. Comput. Struct. Eng. Inst. Korea, 21(3), pp.295~304 (2008)
- Oliver MA, "Basic Steps in Geostatistics: The Variogram and Kriging", pp.37~42 (2015), Springer