Assessing temporal data partitioning scenarios for estimating reference evapotranspiration with machine learning techniques in arid regions.

dc.contributor.author	Hossein, M.
dc.contributor.author	Shiri, J.
dc.contributor.author	Martí, P.
dc.contributor.author	Majnooni, A.
dc.date.accessioned	2024-01-16T09:10:52Z
dc.identifier.uri	http://hdl.handle.net/11201/163611
dc.description.abstract	-
dc.format	application/pdf
dc.relation.isformatof	Versió postprint del document publicat a: https://doi.org/10.1016/j.jhydrol.2020.125252
dc.relation.ispartof	Journal of Hydrology, 2020, vol. 590, num. 11, p. 125252
dc.subject.classification	62 - Enginyeria. Tecnologia
dc.subject.other	62 - Engineering. Technology in general
dc.title	Assessing temporal data partitioning scenarios for estimating reference evapotranspiration with machine learning techniques in arid regions.
dc.type	info:eu-repo/semantics/article
dc.type	info:eu-repo/semantics/acceptedVersion
dc.date.updated	2024-01-16T09:10:53Z
dc.date.embargoEndDate	info:eu-repo/date/embargoEnd/2100-01-01
dc.embargo	2100-01-01
dc.rights.accessRights	info:eu-repo/semantics/embargoedAccess
dc.identifier.doi	https://doi.org/10.1016/j.jhydrol.2020.125252