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Enhancing transparency in land use change modeling: Leveraging eXplainable AI techniques for urban growth prediction with spatially distributed insights.

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Title: Enhancing transparency in land use change modeling: Leveraging eXplainable AI techniques for urban growth prediction with spatially distributed insights.
Authors: Wang, Zelin1, zwang51@gmu.edu, Feng, Tianshu2, tfeng@gmu.edu, Safikhani, Abolfazl1, asafikha@gmu.edu, Tepe, Emre1,3, emretepe@ufl.edu
Source: Computers, Environment & Urban Systems; Oct2025, Vol. 121, 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: 187461799
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Enhancing transparency in land use change modeling: Leveraging eXplainable AI techniques for urban growth prediction with spatially distributed insights.
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  Data: <searchLink fieldCode="JN" term="%22Computers%2C+Environment+%26+Urban+Systems%22">Computers, Environment & Urban Systems</searchLink>; Oct2025, Vol. 121, pN.PAG-N.PAG, 1p
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=187461799
RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1016/j.compenvurbsys.2025.102322
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      – Code: eng
        Text: English
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        StartPage: N.PAG
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      – TitleFull: Enhancing transparency in land use change modeling: Leveraging eXplainable AI techniques for urban growth prediction with spatially distributed insights.
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            NameFull: Wang, Zelin
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            NameFull: Feng, Tianshu
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            NameFull: Safikhani, Abolfazl
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            – D: 01
              M: 10
              Text: Oct2025
              Type: published
              Y: 2025
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              Value: 121
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            – TitleFull: Computers, Environment & Urban Systems
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