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Comparison of Analytical, Multiple Polynomial Regression, and Artificial Neural Networks to Estimate Wetting Parameters for Surface and Buried Drip Irrigation across Three Different Soil Textures

dc.contributor.authorShabani, Ali
dc.contributor.authorShahabi Zad, Vahid
dc.contributor.authorSepaskhah, Ali Reza
dc.contributor.authorMahmoudi, Mohammad Reza
dc.contributor.authorRoshanzamir, Mohamad
dc.date.accessioned2026-08-20T11:09:33Z
dc.date.available2026-08-20T11:09:33Z
dc.date.copyright2025/09/24
dc.date.issued2025
dc.identifier.otherJIDEDH.IRENG-10607.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4311772
dc.description.abstractAbstractThis study investigated the wetting patterns of surface and buried drip irrigation systems across three distinct soil textures: sandy loam, loam, and clay loam. The research employed analytical methods, multiple polynomial regression analysis, and ...
dc.publisherAmerican Society of Civil Engineers
dc.titleComparison of Analytical, Multiple Polynomial Regression, and Artificial Neural Networks to Estimate Wetting Parameters for Surface and Buried Drip Irrigation across Three Different Soil Textures
dc.typeJournal Article
dc.journal.volume151
dc.journal.issue6
dc.journal.titleJournal of Irrigation and Drainage Engineering
dc.identifier.doi10.1061/JIDEDH.IRENG-10607
dc.journal.fristpage04025041-1
dc.journal.lastpage04025041-16
dc.page16
dc.treeJournal of Irrigation and Drainage Engineering:;2025:;Volume ( 151 ):;issue: 006
dc.contenttypeFulltext


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