YaBeSH Engineering and Technology Library

    • Journals
    • PaperQuest
    • YSE Standards
    • YaBeSH
    • Login
    View Item 
    •   YE&T Library
    • ASCE
    • Journal of Irrigation and Drainage Engineering
    • View Item
    •   YE&T Library
    • ASCE
    • Journal of Irrigation and Drainage Engineering
    • View Item
    • All Fields
    • Source Title
    • Year
    • Publisher
    • Title
    • Subject
    • Author
    • DOI
    • ISBN
    Advanced Search
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Archive

    Enhancing Soil Water Prediction in Arid Climates Using Multipredictor Machine-Learning Models and SHAP-Based Interpretability

    Source: Journal of Irrigation and Drainage Engineering:;2026:;Volume ( 152 ):;issue: 001::page 04025049-1
    Author:
    Alsumaiei, Abdullah A.
    ,
    Alrumaidhi, Mubarak
    DOI: 10.1061/JIDEDH.IRENG-10619
    Publisher: American Society of Civil Engineers
    Abstract: AbstractEfficient water management in irrigated watersheds requires the timely and precise application of water to optimize crop yields and sustain resources. Although machine-learning (ML) models have shown promise in capturing hydrological dynamics, ...
    • Download: (2.149Mb)
    • Show Full MetaData Hide Full MetaData
    • Get RIS
    • Item Order
    • Go To Publisher
    • Statistics

      Enhancing Soil Water Prediction in Arid Climates Using Multipredictor Machine-Learning Models and SHAP-Based Interpretability

    URI
    https://yetl.yabesh.ir/yetl1/handle/yetl/4311775
    Collections
    • Journal of Irrigation and Drainage Engineering

    Show full item record

    contributor authorAlsumaiei, Abdullah A.
    contributor authorAlrumaidhi, Mubarak
    date accessioned2026-08-20T11:09:40Z
    date available2026-08-20T11:09:40Z
    date copyright2025/12/09
    date issued2026
    identifier otherJIDEDH.IRENG-10619.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4311775
    description abstractAbstractEfficient water management in irrigated watersheds requires the timely and precise application of water to optimize crop yields and sustain resources. Although machine-learning (ML) models have shown promise in capturing hydrological dynamics, ...
    publisherAmerican Society of Civil Engineers
    titleEnhancing Soil Water Prediction in Arid Climates Using Multipredictor Machine-Learning Models and SHAP-Based Interpretability
    typeJournal Article
    journal volume152
    journal issue1
    journal titleJournal of Irrigation and Drainage Engineering
    identifier doi10.1061/JIDEDH.IRENG-10619
    journal fristpage04025049-1
    journal lastpage04025049-17
    page17
    treeJournal of Irrigation and Drainage Engineering:;2026:;Volume ( 152 ):;issue: 001
    contenttypeFulltext
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian
     
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian