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Seki, Akiyuki; Mayumi, Akie; Wainwright-Murakami, Haruko*; Saito, Kimiaki; Takemiya, Hiroshi; Idomura, Yasuhiro
Proceedings of Joint International Conference on Supercomputing in Nuclear Applications + Monte Carlo 2020 (SNA + MC 2020), p.158 - 164, 2020/10
We developed a method to estimate the temporal change of the air dose rate at the location with sparse (in time) measurements by using the continuous measurement data from the nearby monitoring post. This method determines an observation model from the correlation between sparse data at the target location and dense data at the monitoring post based on a hierarchical Bayesian model. The developed method was validated against the air dose rate measured at the monitoring posts in Fukushima prefecture from 2012 to 2017. The results showed that the developed method can predict the air dose rate at almost all target locations with an error rate of less than 10%.
Sun, D.*; Wainwright-Murakami, Haruko*; Oroza, C. A.*; Seki, Akiyuki; Mikami, Satoshi; Takemiya, Hiroshi; Saito, Kimiaki
Journal of Environmental Radioactivity, 220-221, p.106281_1 - 106281_8, 2020/09
Times Cited Count:9 Percentile:44.38(Environmental Sciences)We have developed a methodology for optimizing the monitoring locations of radiation air dose-rate monitoring. For the method, we use a Gaussian mixture model to identify the representative locations among multiple environmental variables, such as elevation and land-cover types. Next, we use a Gaussian process model to capture and estimate the heterogeneity of air-dose rates across the domain. Our results have shown that this approach allows us to select monitoring locations in a systematic manner such that the heterogeneity of air dose rates is captured by the minimal number of monitoring locations.
Saito, Kimiaki; Mikami, Satoshi; Ando, Masaki; Matsuda, Norihiro; Kinase, Sakae; Tsuda, Shuichi; Yoshida, Tadayoshi; Sato, Tetsuro*; Seki, Akiyuki; Yamamoto, Hideaki*; et al.
Journal of Environmental Radioactivity, 210, p.105878_1 - 105878_12, 2019/12
Times Cited Count:30 Percentile:81.17(Environmental Sciences)Saito, Kimiaki; Mikami, Satoshi; Ando, Masaki; Matsuda, Norihiro; Kinase, Sakae; Tsuda, Shuichi; Sato, Tetsuro*; Seki, Akiyuki; Sanada, Yukihisa; Wainwright-Murakami, Haruko*; et al.
Journal of Radiation Protection and Research, 44(4), p.128 - 148, 2019/12
Murakami, Haruko*; Ahn, J.*
Proceedings of 16th Pacific Basin Nuclear Conference (PBNC-16) (CD-ROM), 6 Pages, 2008/10
Kanno, Ikuo*; Hishiki, Shigeomi*; Murakami, Haruko*; Sugiura, Osamu*; Murase, Yasuhiro*; Nakamura, Tatsuya; Katagiri, Masaki
Nuclear Instruments and Methods in Physics Research A, 520(1-3), p.93 - 95, 2004/03
Times Cited Count:2 Percentile:18.15(Instruments & Instrumentation)no abstracts in English
Seki, Akiyuki; Suzuki, Kenta; Takahashi, Yoshitomo; Matsubara, Takeshi; Suto, Shigeo; Saito, Kimiaki; Takemiya, Hiroshi; Murakami, Haruko*
no journal, ,
After the Fukushima Daiichi Nuclear Power Plant accident, a lot of monitoring studies had been conducted to collect the precious data which are important for the estimation and prediction of the radionuclide distribution. However, those monitoring databases were not convenient for users because their formats were not unified and they are provided as PDF files. Moreover those databases ware published on the independent websites operated by each organization. JAEA developed the database which provides the monitoring data in an unique format on the same website. The database provides not only numerical data but also visualization data following users' needs.
Seki, Akiyuki; Murakami, Haruko*; Saito, Kimiaki; Takemiya, Hiroshi
no journal, ,
Six years have passed since the accident at the Fukushima Daiichi Nuclear Power Plant, and numerous environmental monitoring results have been published at the website, "Database for Radioactive Substance Monitoring Data". Following the object of each monitoring project, these datasets are characterized by different spatial resolution, temporal resolution, and physical quantities. We examined a method to predict the air dose rate from the accident to the present by integrating the time series of environmental monitoring data.
Seki, Akiyuki; Murakami, Haruko*; Saito, Kimiaki; Takemiya, Hiroshi
no journal, ,
In the monitoring post established by the Nuclear Regulatory Authority, air dose rate measurements have been conducted frequently, and eight years have passed since the Fukushima Daiichi nuclear accident, so a large amount of data is accumulated. We extracted the data that supports the optimization of monitoring posts by temporally integrating the results of these monitoring posts. In particular, we performed temporal integration using the hierarchical Bayesian model for the set of results of the air dose rate by multiple monitoring posts and estimated the results of the air dose rate of one monitoring post from the other. In many monitoring post pairs within 1km, we obtained statistically most reliable air dose rate results and their confidence intervals.
Seki, Akiyuki; Mayumi, Akie; Murakami, Haruko*; Saito, Kimiaki; Takemiya, Hiroshi; Idomura, Yasuhiro
no journal, ,
Estimation of temporally continuous air dose rate for few measurement results was performed by hierarchical Bayesian estimation using the result of monitoring post (MP) that can measure frequently. In order to improve the accuracy of this estimation, a screening program was used to automatically eliminate the measurement results of MP containing outliers. Then, the validity of this estimation method was verified by comparison with estimation by other methods such as two-component model.
Murakami, Haruko*; Sun, D.*; Oroza, C.*; Seki, Akiyuki; Mikami, Satoshi; Takemiya, Hiroshi; Saito, Kimiaki
no journal, ,
In this study, we have developed a methodology for optimizing the monitoring locations of radiation air dose rates. It is based on (1) a Gaussian mixture model to diversify locations across the key environmental controls that are known to influence cesium mobility and distributions, and (2) a Gaussian process model to capture the heterogeneity of radiation air dose rates across the domain.
Murakami, Haruko*; Sun, D.*; Seki, Akiyuki; Takemiya, Hiroshi; Saito, Kimiaki
no journal, ,
This study presents a Bayesian hierarchical method to integrate multiple types of radiation measurements and to estimate the spatiotemporal distribution of radiation air dose rates around the Fukushima Daiichi Nuclear Power Plant. The method incorporates the temporal evolution of dose rates by separating the log-linear decay trend and the fluctuations of air dose rates which are spatially correlated based on adjacent monitoring post data.