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Machine learning as a tool for analysing the impact of environmental parameters on the radon exhalation rate from soil
https://repo.qst.go.jp/records/80365
https://repo.qst.go.jp/records/80365aaed4e82-2504-4f1b-b0a5-cc06340c1225
| Item type | 学術雑誌論文 / Journal Article(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2020-08-25 | |||||
| タイトル | ||||||
| タイトル | Machine learning as a tool for analysing the impact of environmental parameters on the radon exhalation rate from soil | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| 資源タイプ | ||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
| 資源タイプ | journal article | |||||
| アクセス権 | ||||||
| アクセス権 | metadata only access | |||||
| アクセス権URI | http://purl.org/coar/access_right/c_14cb | |||||
| 著者 |
Hosoda, M.
× Hosoda, M.× Tokonami, S.× Suzuki, T.× Janik, Miroslaw× Janik, Miroslaw |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | Interest in radon (Rn) is not limited only to its impact on health and its dose to the public, but due to its properties, the techniques to analyse its behavior can be used in many fields such as radiotherapy, atmospheric physics, geophysics, geohazards, mineral exploration, and even planetary science. Nowadays machine learning methods provide extremely important tools for intelligent environmental data analysis, processing and visualization. We describe application of machine learning to environmental sciences with an emphasis on the radon exhalation rate in order to express responses from multivariable time-series data collected at a measuring site near the Sakurajima volcano (Kagoshima, Japan). |
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| 書誌情報 |
Radiation Measurements 巻 138, 発行日 2020-08 |
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| ISSN | ||||||
| 収録物識別子タイプ | ISSN | |||||
| 収録物識別子 | 1350-4487 | |||||
| DOI | ||||||
| 識別子タイプ | DOI | |||||
| 関連識別子 | 10.1016/j.radmeas.2020.106402 | |||||
| 関連サイト | ||||||
| 識別子タイプ | URI | |||||
| 関連識別子 | https://www.sciencedirect.com/science/article/pii/S1350448720301815?dgcid=author | |||||
