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Propagation of Uncertainty from Systematic Error01:10

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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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Related Experiment Video

Updated: Oct 10, 2025

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification ADCI and Dose Estimation
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Improving the estimation accuracy of multi-nuclide source term estimation method for severe nuclear accidents using

Yongsheng Ling1, Tian Huang2, Qi Yue3

  • 1Department of Nuclear Science and Technology, Nanjing University of Aeronautics and Astronautics, 211106, Nanjing, China; Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, 215021, Suzhou, China.

Journal of Environmental Radioactivity
|December 7, 2021
PubMed
Summary

This study introduces a temporal convolutional network model for estimating radionuclide release rates from nuclear accidents using environmental data. The model achieves high accuracy, with prediction errors below 12% for seven key radionuclides.

Keywords:
Bayesian optimization and hyperbandEnvironmental monitoring dataInterRASNuclear emergency managementSource term inversionTemporal convolutional network

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Area of Science:

  • Nuclear Safety
  • Environmental Monitoring
  • Radiological Assessment

Background:

  • Accurate estimation of radionuclide release rates is critical for emergency response during nuclear accidents.
  • Environmental measurements and meteorological data are essential inputs for source term estimation.

Purpose of the Study:

  • To develop and validate a forecasting model for estimating the release rates of seven specific radionuclides (Kr-88, Te-132, I-131, Xe-133, Cs-137, Ba-140, Ce-144).
  • To optimize the model's performance using advanced hyperparameter tuning techniques.
  • To ensure reliable estimations even with missing environmental data.

Main Methods:

  • A temporal convolutional network (TCN) was employed as the core forecasting model.
  • Bayesian Optimization and Hyperband (BOHB) was utilized for hyperparameter optimization to minimize testing loss.
  • A gradient boosting regression model was integrated to predict missing gamma dose rate data.
  • The International Radiological Assessment System (InterRAS) was used for dataset generation.

Main Results:

  • The optimized TCN model achieved a validation loss of 0.0153.
  • The mean absolute percentage error for predicting seven radionuclides was below 12% at 10 hours.
  • Specific radionuclides (Kr-88, Te-132, Cs-137) showed prediction errors as low as 8%.
  • When initial data points were missing, the gradient boosting model ensured prediction errors remained under 30% for all radionuclides.

Conclusions:

  • The proposed TCN model, optimized with BOHB, provides accurate source term estimations for nuclear accidents.
  • The integration of a gradient boosting model enhances the model's robustness in handling incomplete environmental monitoring data.
  • This approach supports improved emergency decision-making through reliable radionuclide release rate predictions.