Articles | Volume 18, issue 17
https://doi.org/10.5194/gmd-18-5513-2025
https://doi.org/10.5194/gmd-18-5513-2025
Development and technical paper
 | 
02 Sep 2025
Development and technical paper |  | 02 Sep 2025

Accurate and fast prediction of radioactive pollution by kriging coupled with auto-associative models

Raphaël Périllat, Sylvain Girard, and Irène Korsakissok

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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 
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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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