Selection method for observation points using Bayesian LASSO at estimating radiation source distribution from air dose rates

\nWhen we decommission a reactor building, it is essential to identify the radiation source distribution for safety. It has been reported that the source distribution can be predicted from the measured air dose rates at appropriate observation points by minimizing an evaluation function using the Least Absolute Shrinkage and Selection Operator (LASSO). However, it is challenging to decide on suitable points in advance. Therefore, we estimate the posterior distribution from the prior distribution of the source amounts, calculated by the standard LASSO, using the Bayesian LASSO. We then assess the predictive distribution of the air dose rates at the candidate observation points from the posterior distribution. We select the additional observation points based on the variances of the predictive distributions. We confirmed that the method can estimate the source distribution with fewer additional observation points than when adding them randomly in most cases.\n

Publication Details

Journal
Springer Link (Chiba Institute of Technology)
Published
2024-10-15
DOI
https://doi.org/10.1051/epjconf/202430216004/pdf
Primary Topic
Genetic and Environmental Crop Studies
Type
article
Field-Weighted Citation Impact
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article

Selection method for observation points using Bayesian LASSO at estimating radiation source distribution from air dose rates

Springer Link (Chiba Institute of Technology)
Genetic and Environmental Crop Studies
article

Selection method for observation points using Bayesian LASSO at estimating radiation source distribution from air dose rates

article en

Abstract

\nWhen we decommission a reactor building, it is essential to identify the radiation source distribution for safety. It has been reported that the source distribution can be predicted from the measured air dose rates at appropriate observation points by minimizing an evaluation function using the Least Absolute Shrinkage and Selection Operator (LASSO). However, it is challenging to decide on suitable points in advance. Therefore, we estimate the posterior distribution from the prior distribution of the source amounts, calculated by the standard LASSO, using the Bayesian LASSO. We then assess the predictive distribution of the air dose rates at the candidate observation points from the posterior distribution. We select the additional observation points based on the variances of the predictive distributions. We confirmed that the method can estimate the source distribution with fewer additional observation points than when adding them randomly in most cases.\n

Springer Link (Chiba Institute of Technology)
Clean water and sanitation
Openalex Percentile: Top 40%
Genetic and Environmental Crop Studies
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