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Comparative Evaluation of Machine-Learning Models for Predicting Daily Evapotranspiration in a Naturally Ventilated Greenhouse

dc.contributor.authorSahoo, Bibhuti Bhusan
dc.contributor.authorNajafzadeh, Mohammad
dc.contributor.authorDT, Santosh
dc.contributor.authorJithendra, Thandra
dc.contributor.authorPanigrahi, Banamali
dc.contributor.authorMishra, Shuchi
dc.contributor.authorGupta, Sushindra Kumar
dc.contributor.authorBhushan, Mani
dc.date.accessioned2026-08-20T11:08:41Z
dc.date.available2026-08-20T11:08:41Z
dc.date.copyright2025/06/28
dc.date.issued2025
dc.identifier.otherJIDEDH.IRENG-10441.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4311752
dc.description.abstractAbstractAccurate determination of reference evapotranspiration (ET0) is crucial for optimizing irrigation scheduling in greenhouse environments, ensuring optimal plant growth and resource management. This study aims to identify the most accurate method for ...
dc.publisherAmerican Society of Civil Engineers
dc.titleComparative Evaluation of Machine-Learning Models for Predicting Daily Evapotranspiration in a Naturally Ventilated Greenhouse
dc.typeJournal Article
dc.journal.volume151
dc.journal.issue5
dc.journal.titleJournal of Irrigation and Drainage Engineering
dc.identifier.doi10.1061/JIDEDH.IRENG-10441
dc.journal.fristpage04025024-1
dc.journal.lastpage04025024-15
dc.page15
dc.treeJournal of Irrigation and Drainage Engineering:;2025:;Volume ( 151 ):;issue: 005
dc.contenttypeFulltext


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