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Cyanotoxin level prediction in a reservoir using gradient boosted regression trees: a case study

dc.contributor.authorGarcía Nieto, Paulino José 
dc.contributor.authorGarcía Gonzalo, María Esperanza 
dc.contributor.authorSánchez Lasheras, Fernando 
dc.contributor.authorAlonso Fernández, José Ramón
dc.contributor.authorDíaz Muñiz, Cristina
dc.contributor.authorCos Juez, Francisco Javier de 
dc.date.accessioned2018-10-08T10:23:06Z
dc.date.available2018-10-08T10:23:06Z
dc.date.issued2018
dc.identifier.citationEnvironmental Science and Pollution Research 25(23), p. 22658–22671 (2018); doi:10.1007/s11356-018-2219-4
dc.identifier.issn0944-1344
dc.identifier.urihttp://hdl.handle.net/10651/48729
dc.format.extentp. 22658–22671
dc.language.isoeng
dc.relation.ispartofEnvironmental Science and Pollution Research, 25
dc.rights© Springer-Verlag GmbH Germany, part of Springer Nature 2018
dc.sourceScopus
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85047799795&doi=10.1007%2fs11356-018-2219-4&partnerID=40&md5=b73e3c839e85bdb0e8da32ff5e7c6b9b
dc.titleCyanotoxin level prediction in a reservoir using gradient boosted regression trees: a case study
dc.typeinfo:eu-repo/semantics/article
dc.identifier.doi10.1007/s11356-018-2219-4
dc.type.dcmitext
dc.relation.publisherversionhttp://dx.doi.org/10.1007/s11356-018-2219-4


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