Predicting the Distribution Coefficient of Cesium in Solid Phase Groups Using Machine Learning

被引：0

作者：

Hong, Seok Min ^{[1
]}

Yoon, In-Ho ^{[3
]}

Cho, Kyung Hwa ^{[2
,4
]}

机构：

[1] Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan,689-798, Korea, Republic of

[2] Graduate School of Carbon Neutrality, Ulsan National Institute of Science and Technology, Ulsan,689-798, Korea, Republic of

[3] Korea Atomic Energy Research Institute, Daejeon, Korea, Republic of

[4] Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, 50 UNIST-gil, Eonyang-eup, Ulju-gun, Ulsan,689-798, Korea, Republic of

来源：

SSRN | 2023年

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D O I：

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中图分类号：

学科分类号：

摘要：

Cesium - Forecasting - Forestry - Radioactivity - Radioisotopes - Random forests - Risk assessment - Waste disposal

引用

共 2 条

[1] Predicting students’ performance at higher education institutions using a machine learning approach
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Kybernetes, 2024,
[2] Predicting the impact of the third wave of COVID-19 in India using hybrid statistical machine learning models: A time series forecasting and sentiment analysis approach
Mohan, Sumit
Solanki, Anil Kumar
Taluja, Harish Kumar
Anuradha
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Computers in Biology and Medicine, 2022, 144

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