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    Performance Evaluation and Uncertainty Quantification of Deep Learning Models for Agricultural Drought Prediction

    Source: Journal of Hydrologic Engineering:;2026:;Volume ( 031 ):;issue: 004::page 04026013-1
    Author:
    Das K., Saranya
    ,
    Chithra, N. R.
    DOI: 10.1061/JHYEFF.HEENG-6751
    Publisher: American Society of Civil Engineers
    Abstract: AbstractEarly prediction of agricultural drought is critical for minimizing its adverse impacts. Although numerous studies have addressed drought forecasting, limited attention has been given to the uncertainty analysis of predictive models. This study ...
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      Performance Evaluation and Uncertainty Quantification of Deep Learning Models for Agricultural Drought Prediction

    URI
    https://yetl.yabesh.ir/yetl1/handle/yetl/4311733
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    contributor authorDas K., Saranya
    contributor authorChithra, N. R.
    date accessioned2026-08-20T11:07:56Z
    date available2026-08-20T11:07:56Z
    date copyright2026/05/16
    date issued2026
    identifier otherJHYEFF.HEENG-6751.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4311733
    description abstractAbstractEarly prediction of agricultural drought is critical for minimizing its adverse impacts. Although numerous studies have addressed drought forecasting, limited attention has been given to the uncertainty analysis of predictive models. This study ...
    publisherAmerican Society of Civil Engineers
    titlePerformance Evaluation and Uncertainty Quantification of Deep Learning Models for Agricultural Drought Prediction
    typeJournal Article
    journal volume31
    journal issue4
    journal titleJournal of Hydrologic Engineering
    identifier doi10.1061/JHYEFF.HEENG-6751
    journal fristpage04026013-1
    journal lastpage04026013-13
    page13
    treeJournal of Hydrologic Engineering:;2026:;Volume ( 031 ):;issue: 004
    contenttypeFulltext
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