Yapay Sinir Ağları ile Kentsel Büyümenin Modellenmesi - Kırklareli Örneği

Year 2023, Volume: 2 Issue: 1, 17 - 30, 30.06.2023

Azem Kuru

Abstract

Kentsel alanların ve kentli nüfusunun sürekli arttığı günümüz dünyasında kentsel büyümeyi yönlendirmek sürdürülebilir bir dünya için zorunluluk arz etmektedir. Kentler içinde barındırdığı birçok etkenin etkisiyle zaman içerisinde yatayda ve dikeyde büyümektedir. Mekânsal olarak büyüyen kentlerin bu büyümesinin doğru yönlendirilmemesi çevresel kaynakların sürdürülebilirliğine olumsuz etki ettiği gibi sosyal ve ekonomik zorlukları da beraberinde getirmektedir. Son yıllarda kentsel büyümenin önceden tahmin edilebilmesi için birçok model oluşturulmuştur. Bu çalışmada yapay sinir ağları teknolojisi kullanılarak Kırklareli kenti için mekânsal büyüme modellenmiştir. 1993 ve 2017 uydu görüntüleri ve hava fotoğrafları yardımıyla tespit edilen kent sınırları yapay sinir ağları ile test edilmiştir. Çalışma sonucunda yapay sinir ağlarının gelecek dönemlerde kentin yayılma sınırlarının belirlenmesinde araç olarak kullanılabilirliği saptanmıştır

Keywords

Yapay sinir ağları, kentsel büyüme tahmini, kentsel büyüme modeli, CBS, Kırklareli.

Supporting Institution

Yok

Project Number

Yok

Thanks

Yok

Urban Growth Prediction with Artificial Neural Networks – Kırklareli Case Study

Abstract

It is a necessity for a sustainable world to manage urban growth, where urban areas and urban populations are constantly increasing. Cities are growing vertically and horizontally over time due to the many factors that they have inhabited. If the spatial growth of cities is not guided correctly, it may have a negative impact on the sustainability of environmental resources as well as social and economic difficulties. In recent years, many models have been created to predict urban growth. In this study, spatial growth for Kırklareli was modeled by using artificial neural network technology. Urban boundaries detected by satellite images and aerial photographs in 1993 and 2017 were tested with artificial neural networks. As a result of the study, the usability of artificial neural networks as a tool to determine the future spatial boundary of cities has been detected.

Keywords

Keywords: Artificial neural networks, urban growth prediction, urban growth modelling, GIS

Project Number

Yok

References

• Almeida, C. M. D. & Gleriani, J. M. (2005). Cellular automata and neural networks as a modelling framework for the simulation of urban land use change.
• Almeida, C. D., Gleriani, J. M., Castejon, E. F., & Soares‐Filho, B. S. (2008). Using neural networks and cellular automata for modelling intra‐urban land‐use dynamics. International Journal of Geographical Information Science, 22(9), 943-963.
• Ataseven, B. (2013). Yapay sinir ağları ile öngörü modellemesi.
• Ayazlı, İ. E., Batuk, F., & Demir, H. (2011). Kentsel Yayılma Simülasyon Modelleri ve Hücresel Otomat.
• Aydın, O. (2015). Karmaşık kent sistemi, kentsel büyüme kavramlarının anlaşılması ve kent modelleme teknikleri. Türk Coğrafya Dergisi, (64).
• Berberoğlu, S., Akın, A., & Clarke, K. C. (2016). Cellular automata modeling approaches to forecast urban growth for Adana, Turkey: A comparative approach. Landscape and Urban Planning, 153, 11-27.
• Dai, E., Wu, S., Shi, W., Cheung, C. K., & Shaker, A. (2005). Modeling change-pattern-value dynamics on land use: an integrated GIS and artificial neural networks approach. Environmental Management, 36(4), 576-591.
• Fischer M.M. (1992). Expert System and Artficial Neural Networks for Spatial Analysis and Modelling: Essential Coponents for Knowledge-Based Geographical Information Systems. Discussion Papers, Vienna University of Economics and Business Administration.
• Fletcher, D., & Goss, E. (1993). Forecasting with neural networks: an application using bankruptcy data. Information & Management, 24(3), 159-167.
• Guan, Q., Wang, L., & Clarke, K. C. (2005). An artificial-neural-network-based, constrained CA model for simulating urban growth. Cartography and Geographic Information Science, 32(4), 369-380.
• Grekousis, G., Manetos, P., & Photis, Y. N. (2013). Modeling urban evolution using neural networks, fuzzy logic and GIS: The case of the Athens metropolitan area. Cities, 30, 193-203.
• Li, X., & Yeh, A. G. O. (2001). Calibration of cellular automata by using neural networks for the simulation of complex urban systems. Environment and Planning A, 33(8), 1445-1462.
• Li, X., & Yeh, A. G. O. (2002). Neural-network-based cellular automata for simulating multiple land use changes using GIS. International Journal of Geographical Information Science, 16(4), 323-343.
• Liu, X., & Lathrop Jr, R. G. (2002). Urban change detection based on an artificial neural network. International Journal of Remote Sensing, 23(12), 2513-2518.
• Maithani, S., Jain, R. K., & Arora, M. K. (2007). An artificial neural network based approach for modeling urban spatial growth. ITPI Journal, 4(2), 43-51.
• Matlab (2018), https://www.mathworks.com/help/matlab Accessed 02.01.2018
• Mohammady, S., Delavar, M. R., & Pahlavani, P. (2014). Urban Growth Modeling Using AN Artificial Neural Network a Case Study of Sanandaj City, Iran. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(2), 203.7
• Moghaddam, H. K., & Samadzadegan, F. (2009). Urban simulation using neural networks and cellular automata for land use planning, 571-577.
• Openshaw, S. (1998). Neural network, genetic, and fuzzy logic models of spatial interaction. Environment and Planning A, 30(10), 1857-1872.
• Özcan, H. (2015). İstanbul'da Kentsel Yayılmanın Yapay Sinir Ağları İle Öngörüleri (Doctoral dissertation, Fen Bilimleri Enstitüsü).
• Öztemel, E. (2003). Yapay Sinir Ağlari. PapatyaYayincilik, Istanbul.
• Park, S., Jeon, S., Kim, S., & Choi, C. (2011). Prediction and comparison of urban growth by land suitability index mapping using GIS and RS in South Korea. Landscape and urban planning, 99(2), 104-114.
• Pijanowski, B. C., Brown, D. G., Shellito, B. A., & Manik, G. A. (2002). Using neural networks and GIS to forecast land use changes: a land transformation model. Computers, environment and urban systems, 26(6), 553-575.
• Pijanowski, B. C., Pithadia, S., Shellito, B. A., & Alexandridis, K. (2005). Calibrating a neural network‐based urban change model for two metropolitan areas of the Upper Midwest of the United States. International Journal of Geographical Information Science, 19(2), 197-215.
• Pijanowski, B. C., Tayyebi, A., Delavar, M. R., & Yazdanpanah, M. J. (2009). Urban expansion simulation using geospatial information system and artificial neural networks. International Journal of Environmental Research, 3(4), 493-502.
• Pijanowski, B. C., Tayyebi, A., Doucette, J., Pekin, B. K., Braun, D., & Plourde, J. (2014). A big data urban growth simulation at a national scale: configuring the GIS and neural network based land transformation model to run in a high performance computing (HPC) environment. Environmental Modelling & Software, 51, 250-268.
• Rafiee, R., Mahiny, A. S., Khorasani, N., Darvishsefat, A. A., & Danekar, A. (2009). Simulating urban growth in Mashad City, Iran through the SLEUTH model (UGM). Cities, 26(1), 19-26.
• Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533.
• Silva, E. A., & Clarke, K. C. (2002). Calibration of the SLEUTH urban growth model for Lisbon and Porto, Portugal. Computers, Environment and Urban Systems, 26(6), 525-552.
• Tayyebi, A., Pijanowski, B. C., & Tayyebi, A. H. (2011). An urban growth boundary model using neural networks, GIS and radial parameterization: An application to Tehran, Iran. Landscape and Urban Planning, 100(1), 35-44.
• Triantakonstantis, D., & Stathakis, D. (2015). Urban growth prediction in Athens, Greece, using artificial neural networks. International Journal of Civil, Environmental, Structural, Construction and Architectural Engineering, 9(3).
• Topuz, S. (2008). İstanbul İlindeki Toplu Taşıma Yolculuk Taleplerinin Yapay Sinir Ağlarıyla Modellenmesi (Doctoral dissertation, Fen Bilimleri Enstitüsü).
• TÜİK (2018), http://www.tuik.gov.tr/ Accessed 02.01.2018
• Veldkamp, A., & Fresco, L. O. (1996). CLUE: a conceptual model to study the conversion of land use and its effects. Ecological modelling, 85(2-3), 253-270.
• Yang, Q., Li, X., & Shi, X. (2008). Cellular automata for simulating land use changes based on support vector machines. Computers & geosciences, 34(6), 592-602.
• Wang, J., & Mountrakis, G. (2011). Developing a multi-network urbanization model: a case study of urban growth in Denver, Colorado. International Journal of Geographical Information Science, 25(2), 229-253.
• Watkiss, B. M. (2008). The SLEUTH urban growth model as forecasting and decision-making tool (Doctoral dissertation, Stellenbosch: Stellenbosch University).
• Wu, X., Hu, Y., He, H. S., Bu, R., Onsted, J., & Xi, F. (2009). Performance evaluation of the SLEUTH model in the Shenyang metropolitan area of northeastern China. Environmental modeling & assessment, 14(2), 221-230.
• Zhang, X. (2016). Urban Growth Modeling Using Neural Network Simulation: A Case Study of Dongguan City, China. Journal of Geographic Information System, 8(03), 317.
• Zhou, L. (2012). Integratıng Artıfıcıal Neural Networks, Image Analysıs and GIS for Urban Spatıal Growth Characterızatıon.
• Xibao, X., Feng, Z., & Jianming, Z. (2006, July). Modelling the impacts of different policy scenarios on urban growth in Lanzhou with remote sensing and Cellular Automata. In Geoscience and Remote Sensing Symposium, 2006. IGARSS 2006. IEEE International Conference on (pp. 1435-1438). IEEE.
• Xi, F., He, H. S., Hu, Y., Wu, X., Bu, R., Chang, Y., & Liu, M. (2009, May). Simulate urban growth based on RS, GIS, and SLEUTH model in Shenyang-Fushun metropolitan area northeastern China. In Urban Remote Sensing Event, 2009 Joint (pp. 1-10). IEEE.