Mapping of groundwater productivity potential with machine learning algorithms: A case study in the provincial capital of Baluchistan, Pakistan

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dc.contributor.areaEducación
dc.contributor.authorTrabucco Ferro, Juan Carlos
dc.contributor.departmentDepartamento de Matemáticas
dc.contributor.facultyFacultad de Ciencias
dc.date.available2024-04-18T10:11:25Z
dc.date.issued2022
dc.description.indexedINDEXADA EN Scopus Web of Science (Science Citation Index, Arts and Humanities Citation Index y Social Science Citation Index).
dc.identifier.urihttps://saber.unimet.edu.ve/handle/20.500.14516/1670
dc.language.isoeng
dc.publication.titleRevista: ELSEVIER / Chemosphere, Volumen 303, Parte 3, Septiembre 2022, 135265
dc.relatedURLhttps://www.sciencedirect.com/science/article/abs/pii/S0045653522017581?via%3Dihub
dc.titleMapping of groundwater productivity potential with machine learning algorithms: A case study in the provincial capital of Baluchistan, Pakistan

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