Preprints
https://doi.org/10.5194/egusphere-2024-3838
https://doi.org/10.5194/egusphere-2024-3838
21 Feb 2025
 | 21 Feb 2025

Accurate and fast prediction of radioactive pollution by Kriging coupled with Auto-Associative Models

Raphaël Périllat, Sylvain Girard, and Irène Korsakissok

Abstract. Uncertainty estimation is a key issue in nuclear crisis situations. Probabilistic methods for taking uncertainties into account in assessments are often costly in terms of the number of simulations and computation time. This is why emulation methods, which enable rapid estimation of numerical model outputs, represent a promising solution. The main limitation of emulation methods is that they can only predict scalar quantities. In a crisis context, decisions are often based on dose maps, which are mathematically represented by high-dimensional data. In this study, we use the Auto-Associative Model method to reduce the dimension of dose results, in order to then predict these reduced data by Kriging. We also compare this prediction method with others used by the French Nuclear Safety and Radiation Protection Authority (ASNR) to predict the consequences of a nuclear accident.

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Journal article(s) based on this preprint

02 Sep 2025
Accurate and fast prediction of radioactive pollution by kriging coupled with auto-associative models
Raphaël Périllat, Sylvain Girard, and Irène Korsakissok
Geosci. Model Dev., 18, 5513–5525, https://doi.org/10.5194/gmd-18-5513-2025,https://doi.org/10.5194/gmd-18-5513-2025, 2025
Short summary
Raphaël Périllat, Sylvain Girard, and Irène Korsakissok

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CEC1: 'Comment on egusphere-2024-3838', Juan Antonio Añel, 05 Mar 2025
  • RC1: 'Comment on egusphere-2024-3838', Anonymous Referee #1, 07 Apr 2025
  • RC2: 'Comment on egusphere-2024-3838', Anonymous Referee #2, 16 Apr 2025
  • AC1: 'Comment on egusphere-2024-3838', Raphaël Périllat, 15 May 2025

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CEC1: 'Comment on egusphere-2024-3838', Juan Antonio Añel, 05 Mar 2025
  • RC1: 'Comment on egusphere-2024-3838', Anonymous Referee #1, 07 Apr 2025
  • RC2: 'Comment on egusphere-2024-3838', Anonymous Referee #2, 16 Apr 2025
  • AC1: 'Comment on egusphere-2024-3838', Raphaël Périllat, 15 May 2025

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Raphaël Périllat on behalf of the Authors (15 May 2025)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (15 May 2025) by Luke Western
RR by Anonymous Referee #2 (31 May 2025)
RR by Anonymous Referee #1 (09 Jun 2025)
ED: Publish subject to technical corrections (09 Jun 2025) by Luke Western
AR by Raphaël Périllat on behalf of the Authors (12 Jun 2025)  Author's response   Manuscript 

Journal article(s) based on this preprint

02 Sep 2025
Accurate and fast prediction of radioactive pollution by kriging coupled with auto-associative models
Raphaël Périllat, Sylvain Girard, and Irène Korsakissok
Geosci. Model Dev., 18, 5513–5525, https://doi.org/10.5194/gmd-18-5513-2025,https://doi.org/10.5194/gmd-18-5513-2025, 2025
Short summary
Raphaël Périllat, Sylvain Girard, and Irène Korsakissok
Raphaël Périllat, Sylvain Girard, and Irène Korsakissok

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Short summary
We developed a method to improve decision-making during nuclear crises by predicting the spread of radiation more efficiently. Existing approaches are often too slow, especially when analyzing complex data like radiation maps. Our method combines techniques to simplify these maps and predict them quickly using statistical tools. This approach could help authorities respond faster and more accurately in emergencies, reducing risks to the population and the environment.
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