Environmental risk assessment and management of nuclear power plants based on big data analysis

Article type: Research Article

Authors: Zhang, Qin | Wu, Han | Ma, Chi | Wang, Yuebin | Zheng, Xiangyang^*

Affiliations: The Nuclear and Radiation Safety Center, Beijing, China

Correspondence: [*] Corresponding author: Xiangyang Zheng, The Nuclear and Radiation Safety Center, Beijing 100000, China. E-mail: [email protected].

Abstract: In traditional research, monitoring data and samples are limited, and it is difficult to achieve ideal results in real-time monitoring and rapid response to environmental risks. By leveraging extensive environmental data gathered from nuclear power plants, the research employed machine learning methodologies for accurate feature selection and extraction of environmental parameters. An efficient environmental risk assessment model was successfully established by using a random forest algorithm. The 95% confidence interval for the area under the curve value spanned from 0.6894 to 0.9292. This provided a more dynamic and effective means for assessing and managing the environmental risks of nuclear power plants.

Keywords: Environmental risk assessment model, monitoring data and samples, big data analysis, nuclear power plants environmental, machine learning

DOI: 10.3233/IDT-240041

Journal: Intelligent Decision Technologies, vol. 18, no. 2, pp. 1259-1269, 2024