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    Comparison of Machine Learning and Empirical Methods for Data-Efficient Estimation of Reference Evapotranspiration in a Semiarid Region

    Source: Journal of Irrigation and Drainage Engineering:;2026:;Volume ( 152 ):;issue: 003::page 04026006-1
    Author:
    Pinarlik, Murat
    ,
    Bostancioglu, Burak
    ,
    Adeloye, Adebayo J.
    ,
    Selek, Bulent
    DOI: 10.1061/JIDEDH.IRENG-10693
    Publisher: American Society of Civil Engineers
    Abstract: AbstractAccurate estimation of reference evapotranspiration (ET0) is critical for efficient irrigation planning and water resource management, particularly in semiarid regions where meteorological data are limited. This study compares the performance of ...Practical ApplicationsThis study presents a practical solution for regions with limited weather data by showing how ML can accurately estimate reference ET0, a key factor in managing water for agriculture, drought planning, and irrigation. Traditional ...
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      Comparison of Machine Learning and Empirical Methods for Data-Efficient Estimation of Reference Evapotranspiration in a Semiarid Region

    URI
    https://yetl.yabesh.ir/yetl1/handle/yetl/4311788
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    • Journal of Irrigation and Drainage Engineering

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    contributor authorPinarlik, Murat
    contributor authorBostancioglu, Burak
    contributor authorAdeloye, Adebayo J.
    contributor authorSelek, Bulent
    date accessioned2026-08-20T11:10:12Z
    date available2026-08-20T11:10:12Z
    date copyright2026/04/07
    date issued2026
    identifier otherJIDEDH.IRENG-10693.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4311788
    description abstractAbstractAccurate estimation of reference evapotranspiration (ET0) is critical for efficient irrigation planning and water resource management, particularly in semiarid regions where meteorological data are limited. This study compares the performance of ...Practical ApplicationsThis study presents a practical solution for regions with limited weather data by showing how ML can accurately estimate reference ET0, a key factor in managing water for agriculture, drought planning, and irrigation. Traditional ...
    publisherAmerican Society of Civil Engineers
    titleComparison of Machine Learning and Empirical Methods for Data-Efficient Estimation of Reference Evapotranspiration in a Semiarid Region
    typeJournal Article
    journal volume152
    journal issue3
    journal titleJournal of Irrigation and Drainage Engineering
    identifier doi10.1061/JIDEDH.IRENG-10693
    journal fristpage04026006-1
    journal lastpage04026006-12
    page12
    treeJournal of Irrigation and Drainage Engineering:;2026:;Volume ( 152 ):;issue: 003
    contenttypeFulltext
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