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Probabilistic Physics‐Guided Deep Neural Networks With Recurrence and Attention Mechanisms for Interpretable Daily Streamflow Simulation.

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Title: Probabilistic Physics‐Guided Deep Neural Networks With Recurrence and Attention Mechanisms for Interpretable Daily Streamflow Simulation.
Authors: Sadeghi Tabas, Sadegh1,2, Samadi, Vidya3,4, samadi@clemson.edu, Wilson, Catherine5, Bhattacharya, Biswa6
Source: Water Resources Research; Sep2025, Vol. 61 Issue 9, p1-30, 30p
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 188293067
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Probabilistic Physics‐Guided Deep Neural Networks With Recurrence and Attention Mechanisms for Interpretable Daily Streamflow Simulation.
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  Data: <searchLink fieldCode="JN" term="%22Water+Resources+Research%22">Water Resources Research</searchLink>; Sep2025, Vol. 61 Issue 9, p1-30, 30p
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=188293067
RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1029/2025WR040173
    Languages:
      – Code: eng
        Text: English
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        PageCount: 30
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      – TitleFull: Probabilistic Physics‐Guided Deep Neural Networks With Recurrence and Attention Mechanisms for Interpretable Daily Streamflow Simulation.
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            NameFull: Sadeghi Tabas, Sadegh
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            NameFull: Samadi, Vidya
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            NameFull: Wilson, Catherine
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            – D: 01
              M: 09
              Text: Sep2025
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              Y: 2025
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              Value: 61
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              Value: 9
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            – TitleFull: Water Resources Research
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