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. |
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| Authors: | Wang, Zelin1 (AUTHOR) zwang51@gmu.edu, Feng, Tianshu2 (AUTHOR) tfeng@gmu.edu, Safikhani, Abolfazl1 (AUTHOR) asafikha@gmu.edu, Tepe, Emre1,3 (AUTHOR) emretepe@ufl.edu |
| Source: | Computers, Environment & Urban Systems. Oct2025, Vol. 121, pN.PAG-N.PAG. 1p. |
| Subjects: | Urban growth, Machine learning, Parameterization, Geospatial data, Land use, Deep learning, Artificial intelligence |
| Abstract: | Recent applications of machine learning (ML) and deep learning (DL) techniques in land-use change modeling have demonstrated significant success in capturing the intricate dynamics of land development. However, their "black-box" nature restricts their utility in various contexts, such as uncovering the underlying drivers of urban expansion. To mitigate this issue, we propose to utilize eXplainable AI (XAI) techniques in ML/DL methods, which presents a promising solution to this primary constraint. To that end, we introduce DL methods to investigate and predict the non-linear dynamics of land use changes. These methods achieved notably high accuracy scores and were more computationally viable than traditional statistical approaches. Moreover, the proposed approach employed in this study surpassed the parameter interpretation capabilities of statistical methods. More specifically, the proposed XAI approach not only highlights the average effects of features on the outcome but also elucidates the factors influencing specific decisions regarding land use changes, including the number of vacant parcels, the share of single-family parcels, and certain time-lagged neighborhood features. Such analyses provide invaluable insights for researchers, practitioners, and policymakers. • Explainable artificial neural network methods to overcome "black-box" nature. • Provide detailed parameter interpretation compared to the statistical methods. • High model accuracy scores with imbalanced land use data. • Offer computationally more feasible alternatives to statistical alternatives. [ABSTRACT FROM AUTHOR] |
| Copyright of Computers, Environment & Urban Systems is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 187461799 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Enhancing transparency in land use change modeling: Leveraging eXplainable AI techniques for urban growth prediction with spatially distributed insights. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wang%2C+Zelin%22">Wang, Zelin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> zwang51@gmu.edu</i><br /><searchLink fieldCode="AR" term="%22Feng%2C+Tianshu%22">Feng, Tianshu</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> tfeng@gmu.edu</i><br /><searchLink fieldCode="AR" term="%22Safikhani%2C+Abolfazl%22">Safikhani, Abolfazl</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> asafikha@gmu.edu</i><br /><searchLink fieldCode="AR" term="%22Tepe%2C+Emre%22">Tepe, Emre</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<i> emretepe@ufl.edu</i> – Name: TitleSource Label: Source Group: Src 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. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Urban+growth%22">Urban growth</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Parameterization%22">Parameterization</searchLink><br /><searchLink fieldCode="DE" term="%22Geospatial+data%22">Geospatial data</searchLink><br /><searchLink fieldCode="DE" term="%22Land+use%22">Land use</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Recent applications of machine learning (ML) and deep learning (DL) techniques in land-use change modeling have demonstrated significant success in capturing the intricate dynamics of land development. However, their "black-box" nature restricts their utility in various contexts, such as uncovering the underlying drivers of urban expansion. To mitigate this issue, we propose to utilize eXplainable AI (XAI) techniques in ML/DL methods, which presents a promising solution to this primary constraint. To that end, we introduce DL methods to investigate and predict the non-linear dynamics of land use changes. These methods achieved notably high accuracy scores and were more computationally viable than traditional statistical approaches. Moreover, the proposed approach employed in this study surpassed the parameter interpretation capabilities of statistical methods. More specifically, the proposed XAI approach not only highlights the average effects of features on the outcome but also elucidates the factors influencing specific decisions regarding land use changes, including the number of vacant parcels, the share of single-family parcels, and certain time-lagged neighborhood features. Such analyses provide invaluable insights for researchers, practitioners, and policymakers. • Explainable artificial neural network methods to overcome "black-box" nature. • Provide detailed parameter interpretation compared to the statistical methods. • High model accuracy scores with imbalanced land use data. • Offer computationally more feasible alternatives to statistical alternatives. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Computers, Environment & Urban Systems is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.compenvurbsys.2025.102322 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Urban growth Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Parameterization Type: general – SubjectFull: Geospatial data Type: general – SubjectFull: Land use Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Artificial intelligence Type: general Titles: – TitleFull: Enhancing transparency in land use change modeling: Leveraging eXplainable AI techniques for urban growth prediction with spatially distributed insights. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wang, Zelin – PersonEntity: Name: NameFull: Feng, Tianshu – PersonEntity: Name: NameFull: Safikhani, Abolfazl – PersonEntity: Name: NameFull: Tepe, Emre IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 01989715 Numbering: – Type: volume Value: 121 Titles: – TitleFull: Computers, Environment & Urban Systems Type: main |
| ResultId | 1 |
