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Prediction methodology of air absorbed dose rates for Chinese cities with deep learning models

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成果类型:
期刊论文
作者:
Guo, Chong;Li, Xiaoyu;Yan, Zhihui;Chen, Lekang;Tang, Bing;...
通讯作者:
Tang, B;Zeng, WJ
作者机构:
[Tang, Bing; Guo, Chong] Univ South China, Affiliated Hosp 2, Hengyang 421001, Peoples R China.
[Li, Xiaoyu; Zeng, Wenjie; Zeng, WJ; Guo, Chong; Yan, Zhihui] Univ South China, Sch Nucl Sci & Technol, Hengyang 421001, Peoples R China.
[Chen, Lekang] Beihang Univ, Sch Phys, Beijing 100191, Peoples R China.
通讯机构:
[Zeng, WJ ; Tang, B ] U
Univ South China, Affiliated Hosp 2, Hengyang 421001, Peoples R China.
Univ South China, Sch Nucl Sci & Technol, Hengyang 421001, Peoples R China.
语种:
英文
关键词:
Air absorbed dose rate;Bi-LSTM;CNN;Deep learning model;Prediction method
期刊:
Journal of Environmental Radioactivity
ISSN:
0265-931X
年:
2025
卷:
286
页码:
107685
基金类别:
National Natural Science Foundation of China [12005096]; Natural Science Foundation of Hunan Province [2025JJ50024]; Key Project of Hunan Provincial Department of Education [24A0292]; Provincial Innovation and Entrepreneurship Training Program for Undergraduates [S202410555201]
机构署名:
本校为第一且通讯机构
院系归属:
核科学技术学院
摘要:
Air absorbed dose rate is a key indicator of environmental radiation exposure. In China, automated environmental radiation monitoring systems have been established in multiple cities to continuously measure air absorbed dose rates. Nevertheless, developing effective preventive strategies based solely on data monitoring remains challenging. To address the issue, this study proposes a prediction framework for urban air absorbed dose rates based on historical data. The framework encompasses model construction, data preprocessing, outcome evaluatio...

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