An Innovative Proposal for Developing a Dynamic Urban Growth Model Through Adaptive Vector Cellular Automata

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dc.contributor.authorYakup, AE
dc.contributor.authorAyazlı, İE
dc.date.accessioned2026-03-31T13:21:12Z
dc.date.available2026-03-31T13:21:12Z
dc.date.issued2025
dc.description.abstractMonitoring urban growth through simulation models is becoming increasingly vital for the sustainable management of cities. Although various raster-based models have been developed over the past three decades, the irregular, fragmented, and heterogeneous geometric structure of urban areas poses significant challenges to effectively modeling complex land use and land cover (LULC) transitions. To address these limitations, this study proposes a novel urban growth simulation model based on vector cellular automata (VCA). In this model, dynamic neighborhood relationships are flexibly established using an algorithm called growth vectors (GVs). Open-access data from four time periods between 1990 and 2018 were utilized for three major European metropolitan areas: Istanbul, Berlin, and Madrid. During the calibration phase, the model was trained using three machine learning algorithms: Random forest, support vector machine, and multi-layer perceptron. For the simulation phase, an adaptive VCA-based urban growth model was developed to predict LULC changes through to 2040. The results demonstrate that the proposed algorithm can achieve a satisfactory level of accuracy in modeling urban growth.
dc.identifier.doi10.3390/ijgi14070259
dc.identifier.issn2220-9964
dc.identifier.issue7
dc.identifier.urihttp://dx.doi.org/10.3390/ijgi14070259
dc.identifier.urihttps://hdl.handle.net/11491/9607
dc.identifier.volume14
dc.identifier.wosWOS:001540017300001
dc.language.isoen
dc.publisherMDPI
dc.relation.ispartofISPRS INT J GEO-INF
dc.subjecturban growth
dc.subjectvector cellular automata
dc.subjectgeo-simulation
dc.subjectmulti-layer perceptron
dc.subjectsupport vector machine
dc.subjectrandom forest
dc.subjectLULC change
dc.titleAn Innovative Proposal for Developing a Dynamic Urban Growth Model Through Adaptive Vector Cellular Automata
dc.typeArticle

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