An effective machine learning model for estimation of reference crop evapotranspiration under limited meteorological parameters

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dc.contributor.guideSaravanan, R
dc.coverage.spatialAn effective machine learning model for estimation of reference crop evapotranspiration under limited meteorological parameters
dc.creator.researcherSaravanan, K
dc.date.accessioned2024-06-18T05:00:22Z
dc.date.available2024-06-18T05:00:22Z
dc.date.awarded2024
dc.date.completed2024
dc.date.registered
dc.description.note
dc.format.accompanyingmaterialNone
dc.format.dimensions21cm.
dc.format.extentxxvi,173p.
dc.identifier.urihttp://hdl.handle.net/10603/571683
dc.languageEnglish
dc.publisher.institutionFaculty of Civil Engineering
dc.publisher.placeChennai
dc.publisher.universityAnna University
dc.relationP.160-172
dc.rightsuniversity
dc.source.universityUniversity
dc.subject.keywordEngineering
dc.subject.keywordEngineering and Technology
dc.subject.keywordEngineering Civil
dc.subject.keywordhydrological cycle
dc.subject.keywordspatiotemporal dynamics
dc.subject.keywordwater-stressed nations
dc.titleAn effective machine learning model for estimation of reference crop evapotranspiration under limited meteorological parameters
dc.title.alternative
dc.type.degreePh.D.

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