Testing machine learning algorithms for the prediction of depositional fluxes of the radionuclides 7Be, 210Pb and 40K.

De La Torre Luque, P; Dueñas, C; Gordo, E; Cañete, S

Testing machine learning algorithms for the prediction of depositional fluxes of the radionuclides ⁷Be, ²¹⁰Pb and ⁴⁰K.

De La Torre Luque, P; Dueñas, C; Gordo, E; Cañete, S.

Afiliación

De La Torre Luque P; The Oskar Klein Centre for Cosmo Particle Physics, Alba Nova, 10691, Stockholm, Sweden. Electronic address: pedro.delatorreluque@fysik.su.se.
Dueñas C; Department of Applied Physics I, Faculty of Sciences, University of Malaga, 29071, Malaga, Spain. Electronic address: mcduenas@uma.es.
Gordo E; Central Research Services (SCAI), University of Malaga, 29071, Malaga, Spain. Electronic address: elisagp@uma.es.
Cañete S; Central Research Services (SCAI), University of Malaga, 29071, Malaga, Spain. Electronic address: scanete@uma.es.

J Environ Radioact ; 265: 107213, 2023 Sep.

Article en En | MEDLINE | ID: mdl-37321136

Asunto(s)

Plomo; Monitoreo de Radiación; Radioisótopos/análisis; Algoritmos; Aprendizaje Automático

Palabras clave

Depositional fluxes; Machine learning techniques; Natural radionuclides

Texto completo

Imprimir

XML

PubMed Links

Buscar en Google

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Monitoreo de Radiación / Plomo Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: J Environ Radioact Asunto de la revista: SAUDE AMBIENTAL Año: 2023 Tipo del documento: Article

Texto completo

Imprimir

XML

PubMed Links

Buscar en Google