From mapping to decision making: a hybrid rule-based and machine learning framework for spatial land-use zoning

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dc.contributor.authorEsen, Fatma
dc.contributor.authorKaradeniz, Enes
dc.contributor.authorSunbul, Fatih
dc.contributor.authorAdiguzel, Asli Deniz
dc.contributor.authorSajjad, Muhammad
dc.date.accessioned2026-04-04T13:31:18Z
dc.date.available2026-04-04T13:31:18Z
dc.date.issued2026
dc.departmentİnönü Üniversitesi
dc.description.abstractThe rapid conversion of land use in coastal regions necessitates advanced decision support frameworks that bridge the gap between mapping and operational zoning. This study introduces the Dual-Logic Spatial Zoning Model (DLSZM), a hybrid framework designed to translate socio-ecological indicators into four planning regimes: Strict Conservation, Managed Use, Development Guidance, and Restoration. Applied to the Antalya region in T & uuml;rkiye at a 30-meter grid resolution, the results demonstrate a high degree of regional convergence between expert-driven and machine learning pathways. Quantitative evaluation via an area-weighted confusion matrix shows that both methods produced identical classifications for Managed Use zones across approximately 7,630 square kilometers. While Managed Use remains the dominant classification, occupying landscapes with moderate ecological value, significant structural divergences were identified in transitional coastal belts. Alluvial transition analysis reveals that the machine learning model, driven by non-linear interactions captured in SHAP analysis, reassigned significant land areas from Strict Conservation and Development categories into the Restoration zone. Specifically, the machine learning framework identifies approximately 2,046 square kilometers of Restoration area, indicating a substantially higher sensitivity to cumulative stressors and degradation signals compared to the expert-derived logic. These findings suggest that while expert systems provide normative clarity, the machine learning pathway offers a more intervention-oriented spatial interpretation, effectively capturing the complex vulnerability dynamics of rapidly transforming coastal environments.
dc.identifier.doi10.3389/fenvs.2026.1791582
dc.identifier.issn2296-665X
dc.identifier.urihttps://doi.org/10.3389/fenvs.2026.1791582
dc.identifier.urihttps://hdl.handle.net/11616/108708
dc.identifier.volume14
dc.identifier.wosWOS:001708749100001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherFrontiers Media Sa
dc.relation.ispartofFrontiers in Environmental Science
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250329
dc.subjectcoastal planning
dc.subjectexplainable AI
dc.subjecthybrid modeling
dc.subjectland-use zoning
dc.subjectmulti-criteria evaluation
dc.subjectspatial analysis
dc.titleFrom mapping to decision making: a hybrid rule-based and machine learning framework for spatial land-use zoning
dc.typeArticle

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