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Machine learning methods as a tool to analyse incomplete or irregularly sampled radon time series data
https://repo.qst.go.jp/records/49099
https://repo.qst.go.jp/records/49099ec473b62-8bcd-4e8b-bf8a-f6269439ec1f
| Item type | 学術雑誌論文 / Journal Article(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2018-07-05 | |||||
| タイトル | ||||||
| タイトル | Machine learning methods as a tool to analyse incomplete or irregularly sampled radon time series data | |||||
| 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 |
Janik, Miroslaw
× Janik, Miroslaw× Bossew, P.× Kurihara, Osamu× Janik, Miroslaw× Kurihara, Osamu |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | In this paper, as our first purpose, we propose the application of machine learning to reconstruct incomplete or irregularly sampled data of time series indoor radon ( 222 Rn). The physical assumption underlying the modelling is that Rn concentration in the air is controlled by environmental variables such as air temperature and pressure. The algorithms “learn” from complete sections of multivariate series, derive a dependence model and apply it to sections where the controls are available, but not the response (Rn), and in this way complete the Rn series. Three machine learning techniques are applied in this study, namely random forest, its extension called the gradient boosting machine and deep learning. For a comparison, we apply the classical multiple regression in a generalized linear model version. Performance of the models is evaluated through different metrics. The performance of the gradient boosting machine is found to be superior to that of the other techniques. |
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| 書誌情報 |
Science of The Total Environment 巻 630, p. 1155-1167, 発行日 2018-07 |
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| 出版者 | ||||||
| 出版者 | Elsevier | |||||
| ISSN | ||||||
| 収録物識別子タイプ | ISSN | |||||
| 収録物識別子 | 0048-9697 | |||||
| DOI | ||||||
| 識別子タイプ | DOI | |||||
| 関連識別子 | 10.1016/j.scitotenv.2018.02.233 | |||||
| 関連サイト | ||||||
| 識別子タイプ | URI | |||||
| 関連識別子 | https://www.sciencedirect.com/science/article/pii/S0048969718306314?via%3Dihub | |||||
