{"type":"ScholarlyArticle","title":"Integrating Atmospheric Dispersion Modeling Priors into Cuboid Splatting for Spatiotemporal Reconstruction of Airborne Radioiodine After Nuclear Accidents","authors":[{"name":"Mareike Böckel","slug":"mareike-bockel","given":"Mareike","family":"Böckel","affiliation":""},{"name":"Stephan Doerfel","slug":"stephan-doerfel","given":"Stephan","family":"Doerfel","affiliation":""},{"name":"Kathrin Meisenberg","slug":"kathrin-meisenberg","given":"Kathrin","family":"Meisenberg","affiliation":""},{"name":"Oliver Meisenberg","slug":"oliver-meisenberg","given":"Oliver","family":"Meisenberg","affiliation":""},{"name":"Max Friedrich","slug":"max-friedrich","given":"Max","family":"Friedrich","affiliation":""},{"name":"Mattis Hartwig","slug":"mattis-hartwig","given":"Mattis","family":"Hartwig","affiliation":""}],"abstract":"In nuclear power plant accidents, airborne radioiodine poses major health risks, making reliable reconstructions of its spatiotemporal distribution crucial for emergency management. Current state-of-the-art prognosis systems use atmospheric dispersion modeling but ignore posterior evidence from emergency care centers, comprising movement profiles and thyroid measurements of affected individuals. A first study showed that the AI method Cuboid Splatting can reconstruct iodine air concentrations from such data but it ignores simulations from established prognosis systems.\r\nOur multidisciplinary team extends Cuboid Splatting by incorporating these simulations as priors and subsequently correcting them using movement and thyroid data. Several ways to translate and correct priors are developed. The best-performing approaches are combined into a novel Cuboid Splatting-with-prior mechanism, which we evaluate using constructed prior scenarios representing different error types and intensities.\r\nUsing Cuboid Splatting-with-prior yields more accurate reconstructions than (i) the used dispersion simulations alone and (ii) plain Cuboid Splatting without prior. Across reconstructions, the mean scenario error is 19.61%, improving on (i) by 28.02pp and on (ii) by 89.86pp, the latter with particularly large gains at high spatial resolution. These results demonstrate that combining simulation-based priors with measurement-based posterior inference can substantially improve the reconstruction of iodine air activity concentrations in nuclear emergencies.","doi":"10.24963/ijcai.2026/787","url":"https://ijcai.org/proceedings/2026/787","pdf":"https://ijcai.org/proceedings/2026/0787.pdf","markdown":"https://ijcai.org/proceedings/2026/787.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/787","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"AI and Social Good","track":"AI and Social Good","number":787,"pages":{"first":7074,"last":7083},"keywords":["Multidisciplinary Topics and Applications: Multidisciplinary Topics and Applications","Data Mining: Data Mining","Machine Learning: Machine Learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}