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Journal of Nuclear Science and Technology Volume 60, 2023 - Issue 10

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Article

Application of Bayesian machine learning for estimation of uncertainty in forecasted plume directions by atmospheric dispersion simulations

Masanao Kadowakia Tokai Research and Development Center, Japan Atomic Energy Agency, Ibaraki, JapanCorrespondence[email protected]

Haruyasu Nagaia Tokai Research and Development Center, Japan Atomic Energy Agency, Ibaraki, Japan

Toshiya Yoshidab Japan Wind Energy Consulting Inc, Tokyo, Japan

Hiroaki Teradaa Tokai Research and Development Center, Japan Atomic Energy Agency, Ibaraki, Japan

Katsunori Tsudukia Tokai Research and Development Center, Japan Atomic Energy Agency, Ibaraki, Japan

Hiroki Sawaa Tokai Research and Development Center, Japan Atomic Energy Agency, Ibaraki, Japan;c KCS Corp, Ibaraki, Japan

Pages 1194-1207 | Received 31 Oct 2022, Accepted 27 Jan 2023, Published online: 12 Mar 2023

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https://doi.org/10.1080/00223131.2023.2177763
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Haruyasu Nagai, Yoshihiro Furuta, Hiromasa Nakayama & Daiki Satoh. (2023) Quantitative visualization of a radioactive plume with harmonizing gamma-ray imaging spectrometry and real-time atmospheric dispersion simulation based on 3D wind observation. Journal of Nuclear Science and Technology 60:11, pages 1345-1360.
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Narakhan Khunsrimek, Wasin Vechgama, Kampanart Silva, Somboon Rassame & Takashi Hibiki. (2023) Simulation using representative data selection for transboundary radiation effect evaluation by Nuclear Accident Consequence Analysis Code (NACAC). Journal of Nuclear Science and Technology 0:0, pages 1-16.
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