Application of Bayesian machine learning for estimation of uncertainty in forecasted plume directions by atmospheric dispersion simulations-Reference-Cited by-同舟云学术

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

Published:2023-03-12 Issue:10 Volume:60 Page:1194-1207
ISSN:0022-3131
Container-title:Journal of Nuclear Science and Technology
language:en
Short-container-title:Journal of Nuclear Science and Technology

Author:

Kadowaki Masanao¹,Nagai Haruyasu¹,Yoshida Toshiya²,Terada Hiroaki¹,Tsuduki Katsunori¹,Sawa Hiroki¹³

Affiliation:

1. Tokai Research and Development Center, Japan Atomic Energy Agency, Ibaraki, Japan

2. Japan Wind Energy Consulting Inc, Tokyo, Japan

3. KCS Corp, Ibaraki, Japan

Publisher

Informa UK Limited

Subject

Nuclear Energy and Engineering,Nuclear and High Energy Physics

Link

https://www.tandfonline.com/doi/pdf/10.1080/00223131.2023.2177763

Reference30 articles.

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2. Development of Scheme for Predicting Atmospheric Dispersion of Radionuclides during Nuclear Emergency by Using Atmospheric Dynamic Model.

3. Headquarters NER report of Japanese government to the IAEA ministerial conference on nuclear safety – The accident at TEPCO’s Fukushima Nuclear Power Stations. Tokyo: Government of Japan; 2011. [cited 2022 Oct 25] Available from: https://japan.kantei.go.jp/kan/topics/201106/iaea_houkokusho_e.html

4. Ensemble dispersion forecasting—Part I: concept, approach and indicators

5. Ensemble dispersion forecasting—Part II: application and evaluation

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