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Innovative optimization-driven machine learning models for hourly streamflow forecasting.

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Title: Innovative optimization-driven machine learning models for hourly streamflow forecasting.
Authors: Parisouj, Peiman1,2, Jun, Changhyun1,3, cjun@korea.ac.kr, Bateni, Sayed M.2,4, Liang, Shunlin5
Source: Knowledge-Based Systems; Apr2026, Vol. 338, pN.PAG-N.PAG, 1p
Database: Applied Science & Technology Source
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  Availability: 0
Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 192302211
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Innovative optimization-driven machine learning models for hourly streamflow forecasting.
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PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=192302211
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.knosys.2026.115487
    Languages:
      – Code: eng
        Text: English
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        PageCount: 1
        StartPage: N.PAG
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      – TitleFull: Innovative optimization-driven machine learning models for hourly streamflow forecasting.
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            NameFull: Parisouj, Peiman
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            NameFull: Jun, Changhyun
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            NameFull: Bateni, Sayed M.
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            NameFull: Liang, Shunlin
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            – D: 08
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              Text: Apr2026
              Type: published
              Y: 2026
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              Value: 09507051
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              Value: 338
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            – TitleFull: Knowledge-Based Systems
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