Statistical approaches to forecast gamma dose rates by using measurements from the atmosphere.
Nuclear Environment Safety Research Center, Korea Atomic Energy Research Institute, 150, Dukjin-dong, Yuseong-gu, Daejeon 305-353, South Korea.Radiation Protection Dosimetry (Impact Factor: 0.91). 08/2008; 131(3):356-64. DOI: 10.1093/rpd/ncn186
In this paper, the results obtained by inter-comparing several statistical techniques for estimating gamma dose rates, such as an exponential moving average model, a seasonal exponential smoothing model and an artificial neural networks model, are reported. Seven years of gamma dose rates data measured in Daejeon City, Korea, were divided into two parts to develop the models and validate the effectiveness of the generated predictions by the techniques mentioned above. Artificial neural networks model shows the best forecasting capability among the three statistical models. The reason why the artificial neural networks model provides a superior prediction to the other models would be its ability for a non-linear approximation. To replace the gamma dose rates when missing data for an environmental monitoring system occurs, the moving average model and the seasonal exponential smoothing model can be better because they are faster and easier for applicability than the artificial neural networks model. These kinds of statistical approaches will be helpful for a real-time control of radio emissions or for an environmental quality assessment.
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ABSTRACT: A radioactivity mapping program was developed to support the decision making in case of a radiological emergency event. Geostatistics and kriging methods were used in the program to make a more accurate radioactivity map for the polluted area. Two variogram models, i.e., linear and exponential models, were tested, and the exponential variogram model showed a better performance when compared with the linear interpolation for estimating unobserved data for radioactivity.Radiation Protection Dosimetry 04/2011; 146(1-3):54-7. DOI:10.1093/rpd/ncr106 · 0.91 Impact Factor
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ABSTRACT: In this paper an approach to model dose distributions, isodose curves and dose uniformity in the Tunisian Gamma Irradiation Facility using artificial neural networks (ANNs) are described. For this purpose, measurements were carried out at different points in the irradiation cell using polymethyl methacrylate dosemeters. The calculated and experimental results are compared and good agreement is observed showing that ANNs can be used as an efficient tool for modelling dose distribution in the gamma irradiation facility. Monte Carlo (MC) photon-transport simulation techniques have been used to evaluate the spatial dose distribution for extensive benchmarking. ANN approach appears to be a significant advance over the time-consuming MC or the less accurate regression methods for dose mapping. As a second application, a detailed dose mapping using two different product densities was carried out. The minimum and maximum dose locations and dose uniformity as a function of the irradiated volume for each product density were determined. Good agreement between ANN modelling and experimental results was achieved.Radiation Protection Dosimetry 04/2013; 157(1). DOI:10.1093/rpd/nct113 · 0.91 Impact Factor
Conference Paper: An Adaptive Smoother for Counting Measurements[Show abstract] [Hide abstract]
ABSTRACT: Counting measurements associated with nuclear instruments are tricky to carry out due to the stochastic process of the radioactivity. Indeed events counting have to be processed and filtered in order to display a stable count rate value and to allow variations monitoring in the measured activity. Smoothers (as the moving average) are adjusted by a time constant defined as a compromise between stability and response time.Proceeding of 3rd International Conference ANIMMA; 06/2013
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