Machine learning methods as a tool to analyse incomplete or irregularly sampled radon time series data.

Janik, M; Bossew, P; Kurihara, O

Janik, M; Bossew, P; Kurihara, O.

Afiliación

Janik M; The National Institutes for Quantum and Radiological Science and Technology (QST), National Institute of Radiological Sciences (NIRS), 4-9-1 Anagawa, Inage-ku, 263-8555 Chiba, Japan. Electronic address: janik.miroslaw@qst.go.jp.
Bossew P; German Federal Office for Radiation Protection (BfS), Koepenicker Allee 120-130, Berlin 10318, Germany.
Kurihara O; The National Institutes for Quantum and Radiological Science and Technology (QST), National Institute of Radiological Sciences (NIRS), 4-9-1 Anagawa, Inage-ku, 263-8555 Chiba, Japan.

Sci Total Environ ; 630: 1155-1167, 2018 Jul 15.

Article en En | MEDLINE | ID: mdl-29554737

Palabras clave

Environment; Linear regression; Machine learning; Neural network; Radon; Sensitivity analysis

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Sci Total Environ Año: 2018 Tipo del documento: Article Pais de publicación: Países Bajos

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