Research Article
BibTex RIS Cite

Year 2026, Volume: 14 Issue: 1, 395 - 412, 01.03.2026
https://doi.org/10.36306/konjes.1705591
https://izlik.org/JA22SL43BN

Abstract

References

  • Y. Xiao, J. L. Zhong, Q. F. Zhang, X. Xiang, and H. Huang, “Exploring the coupling coordination and key factors between urbanization and land use efficiency in ecologically sensitive areas: A case study of the Loess Plateau, China,” Sustain Cities Soc, vol. 86, p. 104148, Nov. 2022, doi: 10.1016/J.SCS.2022.104148.
  • S. Delphin, K. A. Snyder, S. Tanner, K. Musálem, S. E. Marsh, and J. R. Soto, “Obstacles to the development of integrated land-use planning in developing countries: The case of Paraguay,” Land (Basel), vol. 11, no. 8, p. 1339, Aug. 2022, doi: 10.3390/LAND11081339/S1.
  • Ç. Tuğaç, “İklim değişikliği krizi ve şehirler,” Çevre Şehir ve İklim Dergisi, vol. 1, no. 1, pp. 38–60, 2022.
  • Trading Economics, “Turkey Indicators,” Online. Accessed: May 24, 2025. [Online]. Available: https://www.mathworks.com/help/gads/genetic-algorithm.html.
  • M. Burke, A. Driscoll, D. B. Lobell, and S. Ermon, “Using satellite imagery to understand and promote sustainable development,” Science, vol. 371, no. 6535, Mar. 2021, doi: 10.1126/SCIENCE.ABE8628/SUPPL_FILE/ABE8628_BURKE_SM.PDF.
  • W. He et al., “Modeling gridded urban fractional change using the temporal context information in the urban cellular automata model,” Cities, vol. 133, p. 104146, Feb. 2023, doi: 10.1016/J.CITIES.2022.104146.
  • S. Nesmachnow et al., “Urbanization and land use planning for achieving the sustainable development goals (sdgs): A case study of Greece,” Urban Science, vol. 7, no. 2, p. 43, Apr. 2023, doi: 10.3390/URBANSCI7020043.
  • A. Başpinar ve Ş. B. Özvariş, “Covıd-19’un sürdürülebilir kalkınma hedefleri üzerine etkileri”, Sağlık ve Toplum Dergisi, c. 31, s. 2, ss. 3–13, 2021.
  • L. He and X. Zhang, “The distribution effect of urbanization: Theoretical deduction and evidence from China,” Habitat Int, vol. 123, p. 102544, May 2022, doi: 10.1016/J.HABITATINT.2022.102544.
  • C. Akbulak, “Analitik hiyerarşi süreci ve coğrafi bilgi sistemleri ile Yukarı Kara Menderes Havzası’nın arazi kullanımı uygunluk analizi,” Uluslararası İnsan Bilimleri Dergisi, c.7, s.2, ss. 557-576, 2019.
  • S. Çeli̇kyay, S. Cengi̇z, ve S. Görmüş, “Coğrafi Bilgi Sistemleri ile Bartın ilinin arazi kullanım uygunluk analizi,” Bartın Orman Fakültesi Dergisi, vol. 17, no. 25, pp. 73–81, 2015.
  • F. Rachman, J. Huang, X. Xue, and M. A. Marfai, “Insights from 30 years of land use/land cover transitions in Jakarta, Indonesia, via intensity analysis,” Land (Basel), vol. 13, no. 4, p. 545, Apr. 2024, doi: 10.3390/LAND13040545/S1.
  • Y. Zhang, Y. Liu, Y. Zhang, Y. Liu, G. Zhang, and Y. Chen, “On the spatial relationship between ecosystem services and urbanization: A case study in Wuhan, China,” Science of The Total Environment, vol. 637–638, pp. 780–790, Oct. 2018, doi: 10.1016/J.SCITOTENV.2018.04.396.
  • P. Devkota, S. Dhakal, S. Shrestha, and U. B. Shrestha, “Land use land cover changes in the major cities of Nepal from 1990 to 2020,” Environmental and Sustainability Indicators, vol. 17, p. 100227, Feb. 2023, doi: 10.1016/J.INDIC.2023.100227.
  • C. M. Profiroiu, D. A. Bodislav, S. Burlacu, and C. V. Rădulescu, “Challenges of sustainable urban development in the context of population growth,” European Journal of Sustainable Development, vol. 9, no. 3, pp. 51–57, 2020, doi: 10.14207/ejsd.2020.v9n3p51.
  • R. M. Kajiita and S. M. Kang’ethe, “Socio-economic dynamics inhibiting inclusive urban economic development: implications for sustainable urban development in South African cities,” Sustainability, vol.16, no.7, p. 2803, 2024, doi: 10.3390/su16072803.
  • A. I. Almulhim et al., “Charting sustainable urban development through a systematic review of SDG11 research,” Nature Cities, vol. 1, no. 10, pp. 677–685, Aug. 2024, doi: 10.1038/s44284-024-00117-6.
  • R. Almeida, N. R. O. Bastos, M. T. Monteiro, “Modeling some real phenomena by fractional differential equations,” Mathematical Methods in the Applied Sciences, vol. 39, no. 16, pp. 4846-4855, 2016, doi: 10.1002/mma.3818.
  • R. Almeida, A. B. Malinowska, M. Teresa, and T. Monteiro, “Fractional differential equations with a Caputo derivative with respect to a Kernel function and their applications,” Mathematical Methods in the Applied Sciences, vol. 41, no. 1, pp. 336-352, 2018, doi: 10.1002/mma.4617.
  • S. Qureshi, A. Yusuf, and S. Aziz, “Fractional numerical dynamics for the logistic population growth model under Conformable Caputo: a case study with real observations,” Phys Scr, vol. 96, no. 11, p. 114002, Jul. 2021, doi: 10.1088/1402-4896/AC13E0.
  • C. Y. Kee, C. Chua, M. Zubair, and L. K. Ang, “Fractional modeling of urban growth with memory effects,” Chaos, vol. 32, no. 8, p. 83127, Aug. 2022, doi: 10.1063/5.0085933/2836025.
  • L. M. A. Bettencourt, J. Lobo, D. Helbing, C. Kühnert, and G. B. West, “Growth, innovation, scaling, and the pace of life in cities,” Proc Natl Acad Sci U S A, vol. 104, no. 17, pp. 7301–7306, Apr. 2007, doi: 10.1073/PNAS.0610172104/SUPPL_FILE/INDEX.HTML.
  • J. Jafarnezhad, A. Salmanmahiny, and Y. Sakieh, “Subjectivity versus objectivity: Comparative study between brute force method and genetic algorithm for calibrating the SLEUTH urban growth model,” Journal of Urban Planning Development, vol. 142, no. 3, p. 5015015, 2016, doi: 10.1061/(ASCE)UP.1943-5444.0000307.
  • H. Shafizadeh-Moghadam, A. Tayyebi, M. Ahmadlou, M. R. Delavar, and M. Hasanlou, “Integration of genetic algorithm and multiple kernel support vector regression for modeling urban growth,” Comput Environ Urban Syst, vol. 65, pp. 28–40, Sep. 2017, doi: 10.1016/j.compenvurbsys.2017.04.011.
  • R. Pazos-Perez, A. Carballal, J. Rabuñal, L. Alvarez Mures, and D. Garcia-Vidaurrázaga, “Predicting vertical urban growth using genetic evolutionary algorithms in Tokyo’s Minato Ward,” J Urban Plan Dev, vol. 144, p. 4017024, Sep. 2018, doi: 10.1061/(ASCE)UP.1943-5444.0000413.
  • S. Al-Hadidi, G. Sweis, W. Abu-Khader, G. Abu-Rumman, and R. Sweis, “Managing future urbanization growth patterns using genetic algorithm modeling,” Engineering Construction & Architectural Management, Sep. 2023, doi: 10.1108/ECAM-08-2022-0776.
  • M. G. Turner, “Spatial simulation of landscape changes in Georgia: A comparison of 3 transition models,” Landsc Ecol, vol. 1, no. 1, pp. 29–36, Jul. 1987, doi: 10.1007/BF02275263/METRICS.
  • L. Maqelepo, N. Williams, and J. Taneja, “Rural electrification subsidy estimation: a spatial model development and case study,” Environmental Research: Infrastructure and Sustainability, vol. 2, no. 4, p. 045009, Nov. 2022, doi: 10.1088/2634-4505/AC9711.
  • M. Alberti, “Advances in urban ecology: Integrating humans and ecological processes in urban ecosystems,” Advances in Urban Ecology: Integrating Humans and Ecological Processes in Urban Ecosystems, pp. 1–366, 2008, doi: 10.1007/978-0-387-75510-6/COVER.
  • R. Aguejdad, T. Houet, and L. Hubert-Moy, “Spatial validation of land use change models using multiple assessment techniques: a case study of transition potential models,” Environmental Modeling and Assessment, vol. 22, no. 6, pp. 591–606, Dec. 2017, doi: 10.1007/S10666-017-9564-4/FIGURES/13.
  • S. Cengiz, “Kentsel büyüme dinamiklerinin modellenmesi: Ankara kenti simülasyonu,”Fen Bilimleri Enstitüsü. Doktora tezi, 2019.
  • H. Oğuz and N. Bozali, “Prediction of land use/land cover change in the city of Gaziantep until the Year 2040,” Journal Of Agrıcultural Scıences, vol. 20, no. 1, pp. 343–354, 2014, doi: 10.2/JQUERY.MIN.JS.
  • A. A. Kafy et al., “Cellular Automata approach in dynamic modelling of land cover changes using RapidEye images in Dhaka, Bangladesh,” Environmental Challenges, vol. 4, p. 100084, Aug. 2021, doi: 10.1016/J.ENVC.2021.100084.
  • D. Feng, J. Liu, K. Lawson, and C. Shen, “Differentiable, learnable, regionalized process-based models with multiphysical outputs can approach state-of-the-art hydrologic prediction accuracy,” Water Resour Res, vol. 58, no. 10, p. e2022WR032404, Oct. 2022, doi: 10.1029/2022WR032404.
  • A. Bhusal, U. Parajuli, S. Regmi, and A. Kalra, “Application of Machine Learning and Process-Based Models for Rainfall-Runoff Simulation in DuPage River Basin, Illinois,” Hydrology 2022, Vol. 9, Page 117, vol. 9, no. 7, p. 117, Jun. 2022, doi: 10.3390/HYDROLOGY9070117.
  • P. H. Verburg et al., “Beyond land cover change: towards a new generation of land use models,” Curr Opin Environ Sustain, vol. 38, pp. 77–85, Jun. 2019, doi: 10.1016/J.COSUST.2019.05.002.
  • M. Dumansızoğlu, “Gebze organize sanayi bölgesinin gelişim süreci ve mekânsal etkileri,” Master's thesis, Sakarya University, 2017.
  • L. M. A. Bettencourt, “The origins of scaling in cities,” Science (1979), vol. 340, no. 6139, pp. 1438–1441, Jun. 2013, doi: 10.1126/SCIENCE.1235823/SUPPL_FILE/BETTENCOURT.SM.PDF.
  • J. Lobo, L. M. A. Bettencourt, M. E. Smith, and S. Ortman, “Settlement scaling theory: Bridging the study of ancient and contemporary urban systems,” Urban Studies, vol. 57, no. 4, pp. 731–747, Mar. 2020, doi: 10.1177/0042098019873796.
  • S. G. Ortman, J. Lobo, and M. E. Smith, “Cities: Complexity, theory and history,” PLoS One, vol. 15, no. 12, p. e0243621, Dec. 2020, doi: 10.1371/JOURNAL.PONE.0243621.
  • L. M. A. Bettencourt, Introduction to Urban Science: Evidence and Theory of Cities as Complex Systems. MIT Press, Cambridge, 2021.
  • K. Diethelm, N.J. Ford, “The analysis of fractional differential equations,” Journal of Mathematical Analysis and Applications, vol. 265, no. 2, pp. 229-248, 2002, doi: 10.1007/978-3-642-14574-2.
  • MathWorks, “Genetic algorithm-MATLAB&simulink,” Online. Accessed: May 24, 2025. [Online]. Available: https://www.mathworks.com/help/gads/genetic-algorithm.html
  • Holland John Henry, Adaptation in Natural and Artificial Systems: An Introductory Analysis with. University of Michigan Press, 1975.
  • A. Bruzzone, A. Orsoni, R. Mosca, and R. Revetria, “AI-based optimization for fleet management in maritime logistics,” Winter Simulation Conference Proceedings, vol. 2, pp. 1174–1182, 2002, doi: 10.1109/WSC.2002.1166375.
  • A. Prakash, R. Shankar, N. Shukla, and M. K. Tiwari, “Solving machine loading problem of FMS: an artificial intelligence (AI) based random search optimization approach,”, IGI Global, 2008, pp. 19–43. doi: 10.4018/978-1-59904-582-5.CH002.
  • A. M. Sharaf and A. A. A. Elgammal, “Novel AI-based soft computing applications in motor drives,” Power Electronics Handbook, Fourth Edition, pp. 1261–1302, Jan. 2018, doi: 10.1016/B978-0-12-811407-0.00042-8.
  • K. Y. Yap, C. R. Sarimuthu, and J. M. Y. Lim, “Artificial intelligence based MPPT techniques for solar power system: A review,” Journal of Modern Power Systems and Clean Energy, vol. 8, no. 6, pp. 1043–1059, Nov. 2020, doi: 10.35833/MPCE.2020.000159.
  • A. Swarnkar and A. Swarnkar, “Artificial intelligence based optimization techniques: A review,” Lecture Notes in Electrical Engineering, vol. 607, pp. 95–103, 2020, doi: 10.1007/978-981-15-0214-9_12.
  • P. Bhattacharjee, R. K. Jana, and S. Bhattacharya, “A relative analysis of genetic algorithm and binary particle swarm optimization for finding the optimal cost of wind power generation in tirumala area of India,” ITM Web of Conferences, vol. 40, p. 03016, 2021, doi: 10.1051/ITMCONF/20214003016.
  • Z. Qiu et al., “Application of genetic algorithm combined with improved SEIR model in predicting the epidemic trend of COVID-19, China,” Scientific Reports 2022 12:1, vol. 12, no. 1, pp. 1–9, May 2022, doi: 10.1038/s41598-022-12958-z.
  • A. R. Sergio and P. H. T. Schimit, “Optimizing contact network topological parameters of urban populations using the genetic algorithm,” Entropy 2024, Vol. 26, Page 661, vol. 26, no. 8, p. 661, Aug. 2024, doi: 10.3390/E26080661.
  • Turkish Statistical Institute, “Database,” Online, Available: https://biruni.tuik.gov.tr/medas/?locale=tr. Accessed: May 24, 2025. [Online].
  • V. Chaturvedi and W. T. de Vries, “Machine learning algorithms for urban land use planning: A review,” Urban Science 2021, Vol. 5, Page 68, vol. 5, no. 3, p. 68, Sep. 2021, doi: 10.3390/URBANSCI5030068.
  • T. Mollick, M. G. Azam, and S. Karim, “Geospatial-based machine learning techniques for land use and land cover mapping using a high-resolution unmanned aerial vehicle image,” Remote Sens Appl, vol. 29, p. 100859, Jan. 2023, doi: 10.1016/J.RSASE.2022.100859.
  • K. Diethelm, N. J. Ford, and A. D. Freed, “A predictor-corrector approach for the numerical solution of fractional differential equations,” Nonlinear Dyn, vol. 29, no. 1–4, pp. 3–22, Jul. 2002, doi: 10.1023/A:1016592219341/METRICS.

ANALYZING URBAN GROWTH DYNAMICS FOR GEBZE: GEOGRAPHIC INFORMATION SYSTEMS AND AI-BASED PARAMETER OPTIMIZATION

Year 2026, Volume: 14 Issue: 1, 395 - 412, 01.03.2026
https://doi.org/10.36306/konjes.1705591
https://izlik.org/JA22SL43BN

Abstract

This study aims to investigate the effect of population parameters on changes in urban land cover/use in the Gebze district for the years 2010, 2015, and 2024 through spatial analyses and a fractional-order mathematical model. The fractional-order mathematical model is applied to examine the dynamics of urban growth in the district, while model parameters are optimized via an artificial intelligence–based approach. Additionally, the potential urban growth scenarios for Gebze were simulated by MATLAB to project population growth for 2038. Subsequently, changes in land use for the specified years are calculated using Remote Sensing and Geographic Information Systems. In light of the results obtained, industrialisation, among the economic indicators, emerges as a key factor influencing urban population growth and land use change in the Gebze district. The expansion of industrial areas by 95.2% between 2010 and 2024 has driven a 41.2% increase in urban growth, while projections for 2038 estimate that the population of Gebze district will range between 600.000 and over 2.000.000. While the increase in population due to industrial development in the district by 2038 is expected to bring economic benefits, it may also lead to certain ecological and socio-cultural issues.

References

  • Y. Xiao, J. L. Zhong, Q. F. Zhang, X. Xiang, and H. Huang, “Exploring the coupling coordination and key factors between urbanization and land use efficiency in ecologically sensitive areas: A case study of the Loess Plateau, China,” Sustain Cities Soc, vol. 86, p. 104148, Nov. 2022, doi: 10.1016/J.SCS.2022.104148.
  • S. Delphin, K. A. Snyder, S. Tanner, K. Musálem, S. E. Marsh, and J. R. Soto, “Obstacles to the development of integrated land-use planning in developing countries: The case of Paraguay,” Land (Basel), vol. 11, no. 8, p. 1339, Aug. 2022, doi: 10.3390/LAND11081339/S1.
  • Ç. Tuğaç, “İklim değişikliği krizi ve şehirler,” Çevre Şehir ve İklim Dergisi, vol. 1, no. 1, pp. 38–60, 2022.
  • Trading Economics, “Turkey Indicators,” Online. Accessed: May 24, 2025. [Online]. Available: https://www.mathworks.com/help/gads/genetic-algorithm.html.
  • M. Burke, A. Driscoll, D. B. Lobell, and S. Ermon, “Using satellite imagery to understand and promote sustainable development,” Science, vol. 371, no. 6535, Mar. 2021, doi: 10.1126/SCIENCE.ABE8628/SUPPL_FILE/ABE8628_BURKE_SM.PDF.
  • W. He et al., “Modeling gridded urban fractional change using the temporal context information in the urban cellular automata model,” Cities, vol. 133, p. 104146, Feb. 2023, doi: 10.1016/J.CITIES.2022.104146.
  • S. Nesmachnow et al., “Urbanization and land use planning for achieving the sustainable development goals (sdgs): A case study of Greece,” Urban Science, vol. 7, no. 2, p. 43, Apr. 2023, doi: 10.3390/URBANSCI7020043.
  • A. Başpinar ve Ş. B. Özvariş, “Covıd-19’un sürdürülebilir kalkınma hedefleri üzerine etkileri”, Sağlık ve Toplum Dergisi, c. 31, s. 2, ss. 3–13, 2021.
  • L. He and X. Zhang, “The distribution effect of urbanization: Theoretical deduction and evidence from China,” Habitat Int, vol. 123, p. 102544, May 2022, doi: 10.1016/J.HABITATINT.2022.102544.
  • C. Akbulak, “Analitik hiyerarşi süreci ve coğrafi bilgi sistemleri ile Yukarı Kara Menderes Havzası’nın arazi kullanımı uygunluk analizi,” Uluslararası İnsan Bilimleri Dergisi, c.7, s.2, ss. 557-576, 2019.
  • S. Çeli̇kyay, S. Cengi̇z, ve S. Görmüş, “Coğrafi Bilgi Sistemleri ile Bartın ilinin arazi kullanım uygunluk analizi,” Bartın Orman Fakültesi Dergisi, vol. 17, no. 25, pp. 73–81, 2015.
  • F. Rachman, J. Huang, X. Xue, and M. A. Marfai, “Insights from 30 years of land use/land cover transitions in Jakarta, Indonesia, via intensity analysis,” Land (Basel), vol. 13, no. 4, p. 545, Apr. 2024, doi: 10.3390/LAND13040545/S1.
  • Y. Zhang, Y. Liu, Y. Zhang, Y. Liu, G. Zhang, and Y. Chen, “On the spatial relationship between ecosystem services and urbanization: A case study in Wuhan, China,” Science of The Total Environment, vol. 637–638, pp. 780–790, Oct. 2018, doi: 10.1016/J.SCITOTENV.2018.04.396.
  • P. Devkota, S. Dhakal, S. Shrestha, and U. B. Shrestha, “Land use land cover changes in the major cities of Nepal from 1990 to 2020,” Environmental and Sustainability Indicators, vol. 17, p. 100227, Feb. 2023, doi: 10.1016/J.INDIC.2023.100227.
  • C. M. Profiroiu, D. A. Bodislav, S. Burlacu, and C. V. Rădulescu, “Challenges of sustainable urban development in the context of population growth,” European Journal of Sustainable Development, vol. 9, no. 3, pp. 51–57, 2020, doi: 10.14207/ejsd.2020.v9n3p51.
  • R. M. Kajiita and S. M. Kang’ethe, “Socio-economic dynamics inhibiting inclusive urban economic development: implications for sustainable urban development in South African cities,” Sustainability, vol.16, no.7, p. 2803, 2024, doi: 10.3390/su16072803.
  • A. I. Almulhim et al., “Charting sustainable urban development through a systematic review of SDG11 research,” Nature Cities, vol. 1, no. 10, pp. 677–685, Aug. 2024, doi: 10.1038/s44284-024-00117-6.
  • R. Almeida, N. R. O. Bastos, M. T. Monteiro, “Modeling some real phenomena by fractional differential equations,” Mathematical Methods in the Applied Sciences, vol. 39, no. 16, pp. 4846-4855, 2016, doi: 10.1002/mma.3818.
  • R. Almeida, A. B. Malinowska, M. Teresa, and T. Monteiro, “Fractional differential equations with a Caputo derivative with respect to a Kernel function and their applications,” Mathematical Methods in the Applied Sciences, vol. 41, no. 1, pp. 336-352, 2018, doi: 10.1002/mma.4617.
  • S. Qureshi, A. Yusuf, and S. Aziz, “Fractional numerical dynamics for the logistic population growth model under Conformable Caputo: a case study with real observations,” Phys Scr, vol. 96, no. 11, p. 114002, Jul. 2021, doi: 10.1088/1402-4896/AC13E0.
  • C. Y. Kee, C. Chua, M. Zubair, and L. K. Ang, “Fractional modeling of urban growth with memory effects,” Chaos, vol. 32, no. 8, p. 83127, Aug. 2022, doi: 10.1063/5.0085933/2836025.
  • L. M. A. Bettencourt, J. Lobo, D. Helbing, C. Kühnert, and G. B. West, “Growth, innovation, scaling, and the pace of life in cities,” Proc Natl Acad Sci U S A, vol. 104, no. 17, pp. 7301–7306, Apr. 2007, doi: 10.1073/PNAS.0610172104/SUPPL_FILE/INDEX.HTML.
  • J. Jafarnezhad, A. Salmanmahiny, and Y. Sakieh, “Subjectivity versus objectivity: Comparative study between brute force method and genetic algorithm for calibrating the SLEUTH urban growth model,” Journal of Urban Planning Development, vol. 142, no. 3, p. 5015015, 2016, doi: 10.1061/(ASCE)UP.1943-5444.0000307.
  • H. Shafizadeh-Moghadam, A. Tayyebi, M. Ahmadlou, M. R. Delavar, and M. Hasanlou, “Integration of genetic algorithm and multiple kernel support vector regression for modeling urban growth,” Comput Environ Urban Syst, vol. 65, pp. 28–40, Sep. 2017, doi: 10.1016/j.compenvurbsys.2017.04.011.
  • R. Pazos-Perez, A. Carballal, J. Rabuñal, L. Alvarez Mures, and D. Garcia-Vidaurrázaga, “Predicting vertical urban growth using genetic evolutionary algorithms in Tokyo’s Minato Ward,” J Urban Plan Dev, vol. 144, p. 4017024, Sep. 2018, doi: 10.1061/(ASCE)UP.1943-5444.0000413.
  • S. Al-Hadidi, G. Sweis, W. Abu-Khader, G. Abu-Rumman, and R. Sweis, “Managing future urbanization growth patterns using genetic algorithm modeling,” Engineering Construction & Architectural Management, Sep. 2023, doi: 10.1108/ECAM-08-2022-0776.
  • M. G. Turner, “Spatial simulation of landscape changes in Georgia: A comparison of 3 transition models,” Landsc Ecol, vol. 1, no. 1, pp. 29–36, Jul. 1987, doi: 10.1007/BF02275263/METRICS.
  • L. Maqelepo, N. Williams, and J. Taneja, “Rural electrification subsidy estimation: a spatial model development and case study,” Environmental Research: Infrastructure and Sustainability, vol. 2, no. 4, p. 045009, Nov. 2022, doi: 10.1088/2634-4505/AC9711.
  • M. Alberti, “Advances in urban ecology: Integrating humans and ecological processes in urban ecosystems,” Advances in Urban Ecology: Integrating Humans and Ecological Processes in Urban Ecosystems, pp. 1–366, 2008, doi: 10.1007/978-0-387-75510-6/COVER.
  • R. Aguejdad, T. Houet, and L. Hubert-Moy, “Spatial validation of land use change models using multiple assessment techniques: a case study of transition potential models,” Environmental Modeling and Assessment, vol. 22, no. 6, pp. 591–606, Dec. 2017, doi: 10.1007/S10666-017-9564-4/FIGURES/13.
  • S. Cengiz, “Kentsel büyüme dinamiklerinin modellenmesi: Ankara kenti simülasyonu,”Fen Bilimleri Enstitüsü. Doktora tezi, 2019.
  • H. Oğuz and N. Bozali, “Prediction of land use/land cover change in the city of Gaziantep until the Year 2040,” Journal Of Agrıcultural Scıences, vol. 20, no. 1, pp. 343–354, 2014, doi: 10.2/JQUERY.MIN.JS.
  • A. A. Kafy et al., “Cellular Automata approach in dynamic modelling of land cover changes using RapidEye images in Dhaka, Bangladesh,” Environmental Challenges, vol. 4, p. 100084, Aug. 2021, doi: 10.1016/J.ENVC.2021.100084.
  • D. Feng, J. Liu, K. Lawson, and C. Shen, “Differentiable, learnable, regionalized process-based models with multiphysical outputs can approach state-of-the-art hydrologic prediction accuracy,” Water Resour Res, vol. 58, no. 10, p. e2022WR032404, Oct. 2022, doi: 10.1029/2022WR032404.
  • A. Bhusal, U. Parajuli, S. Regmi, and A. Kalra, “Application of Machine Learning and Process-Based Models for Rainfall-Runoff Simulation in DuPage River Basin, Illinois,” Hydrology 2022, Vol. 9, Page 117, vol. 9, no. 7, p. 117, Jun. 2022, doi: 10.3390/HYDROLOGY9070117.
  • P. H. Verburg et al., “Beyond land cover change: towards a new generation of land use models,” Curr Opin Environ Sustain, vol. 38, pp. 77–85, Jun. 2019, doi: 10.1016/J.COSUST.2019.05.002.
  • M. Dumansızoğlu, “Gebze organize sanayi bölgesinin gelişim süreci ve mekânsal etkileri,” Master's thesis, Sakarya University, 2017.
  • L. M. A. Bettencourt, “The origins of scaling in cities,” Science (1979), vol. 340, no. 6139, pp. 1438–1441, Jun. 2013, doi: 10.1126/SCIENCE.1235823/SUPPL_FILE/BETTENCOURT.SM.PDF.
  • J. Lobo, L. M. A. Bettencourt, M. E. Smith, and S. Ortman, “Settlement scaling theory: Bridging the study of ancient and contemporary urban systems,” Urban Studies, vol. 57, no. 4, pp. 731–747, Mar. 2020, doi: 10.1177/0042098019873796.
  • S. G. Ortman, J. Lobo, and M. E. Smith, “Cities: Complexity, theory and history,” PLoS One, vol. 15, no. 12, p. e0243621, Dec. 2020, doi: 10.1371/JOURNAL.PONE.0243621.
  • L. M. A. Bettencourt, Introduction to Urban Science: Evidence and Theory of Cities as Complex Systems. MIT Press, Cambridge, 2021.
  • K. Diethelm, N.J. Ford, “The analysis of fractional differential equations,” Journal of Mathematical Analysis and Applications, vol. 265, no. 2, pp. 229-248, 2002, doi: 10.1007/978-3-642-14574-2.
  • MathWorks, “Genetic algorithm-MATLAB&simulink,” Online. Accessed: May 24, 2025. [Online]. Available: https://www.mathworks.com/help/gads/genetic-algorithm.html
  • Holland John Henry, Adaptation in Natural and Artificial Systems: An Introductory Analysis with. University of Michigan Press, 1975.
  • A. Bruzzone, A. Orsoni, R. Mosca, and R. Revetria, “AI-based optimization for fleet management in maritime logistics,” Winter Simulation Conference Proceedings, vol. 2, pp. 1174–1182, 2002, doi: 10.1109/WSC.2002.1166375.
  • A. Prakash, R. Shankar, N. Shukla, and M. K. Tiwari, “Solving machine loading problem of FMS: an artificial intelligence (AI) based random search optimization approach,”, IGI Global, 2008, pp. 19–43. doi: 10.4018/978-1-59904-582-5.CH002.
  • A. M. Sharaf and A. A. A. Elgammal, “Novel AI-based soft computing applications in motor drives,” Power Electronics Handbook, Fourth Edition, pp. 1261–1302, Jan. 2018, doi: 10.1016/B978-0-12-811407-0.00042-8.
  • K. Y. Yap, C. R. Sarimuthu, and J. M. Y. Lim, “Artificial intelligence based MPPT techniques for solar power system: A review,” Journal of Modern Power Systems and Clean Energy, vol. 8, no. 6, pp. 1043–1059, Nov. 2020, doi: 10.35833/MPCE.2020.000159.
  • A. Swarnkar and A. Swarnkar, “Artificial intelligence based optimization techniques: A review,” Lecture Notes in Electrical Engineering, vol. 607, pp. 95–103, 2020, doi: 10.1007/978-981-15-0214-9_12.
  • P. Bhattacharjee, R. K. Jana, and S. Bhattacharya, “A relative analysis of genetic algorithm and binary particle swarm optimization for finding the optimal cost of wind power generation in tirumala area of India,” ITM Web of Conferences, vol. 40, p. 03016, 2021, doi: 10.1051/ITMCONF/20214003016.
  • Z. Qiu et al., “Application of genetic algorithm combined with improved SEIR model in predicting the epidemic trend of COVID-19, China,” Scientific Reports 2022 12:1, vol. 12, no. 1, pp. 1–9, May 2022, doi: 10.1038/s41598-022-12958-z.
  • A. R. Sergio and P. H. T. Schimit, “Optimizing contact network topological parameters of urban populations using the genetic algorithm,” Entropy 2024, Vol. 26, Page 661, vol. 26, no. 8, p. 661, Aug. 2024, doi: 10.3390/E26080661.
  • Turkish Statistical Institute, “Database,” Online, Available: https://biruni.tuik.gov.tr/medas/?locale=tr. Accessed: May 24, 2025. [Online].
  • V. Chaturvedi and W. T. de Vries, “Machine learning algorithms for urban land use planning: A review,” Urban Science 2021, Vol. 5, Page 68, vol. 5, no. 3, p. 68, Sep. 2021, doi: 10.3390/URBANSCI5030068.
  • T. Mollick, M. G. Azam, and S. Karim, “Geospatial-based machine learning techniques for land use and land cover mapping using a high-resolution unmanned aerial vehicle image,” Remote Sens Appl, vol. 29, p. 100859, Jan. 2023, doi: 10.1016/J.RSASE.2022.100859.
  • K. Diethelm, N. J. Ford, and A. D. Freed, “A predictor-corrector approach for the numerical solution of fractional differential equations,” Nonlinear Dyn, vol. 29, no. 1–4, pp. 3–22, Jul. 2002, doi: 10.1023/A:1016592219341/METRICS.
There are 56 citations in total.

Details

Primary Language English
Subjects Land Management, Numerical Modelling and Mechanical Characterisation
Journal Section Research Article
Authors

Figen Altıner 0000-0002-3744-6415

Dilara Yapışkan

Submission Date May 24, 2025
Acceptance Date October 13, 2025
Publication Date March 1, 2026
DOI https://doi.org/10.36306/konjes.1705591
IZ https://izlik.org/JA22SL43BN
Published in Issue Year 2026 Volume: 14 Issue: 1

Cite

IEEE [1]F. Altıner and D. Yapışkan, “ANALYZING URBAN GROWTH DYNAMICS FOR GEBZE: GEOGRAPHIC INFORMATION SYSTEMS AND AI-BASED PARAMETER OPTIMIZATION”, KONJES, vol. 14, no. 1, pp. 395–412, Mar. 2026, doi: 10.36306/konjes.1705591.