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The use of machine learning to identify suitable areas for urban growth in mountainous areas: Tunceli city example

Yıl 2022, Sayı: 47, 210 - 232, 30.09.2022

Fethi Ahmet Canpolat

https://doi.org/10.32003/igge.1119297

Öz

One of the most important trigger factors contributing to increased human intervention in space in many regions of the world is urbanization. To manage and plan urbanization in harmony with other human activities, it is necessary to manage and plan it accordingly. Even though urbanization studies tend to focus on large cities, small-scale cities are quite common throughout the world, both in terms of their numbers and regarding their population density. Moreover, small cities can contribute to a more homogeneous distribution of development at the national and regional levels. It may, however, be hindered by a variety of limitations, including the hinterlands and the unused potential of these settlements. The city of Tunceli is also a small settlement with natural and human factors limiting its growth. In this study, based on machine learning algorithms, "support vector machines", "artificial neural networks" and "random forest" models were used to determine urban growth zones. In the city, the most suitable sites for primary growth are those which are suited for peripheral growth and inward-stacked growth (12 km2). While more than 90% of predictions were accurate, regarding the spatial equivalents of the findings, the best results respectively, came from "random forests", "artificial neural networks", and finally "support vector machines".

Anahtar Kelimeler

Tunceli urban growth support vector machines artificial neural networks random forest

Kaynakça

  • Akar, Ö. & Güngör, O. (2012). Rastgele orman algoritması kullanılarak çok bantlı görüntülerin sınıflandırılması. Jeodezi ve Jeoinformasyon Dergisi , (106), 139-146 . https://doi.org/10.9733/jgg.241212.1t
  • Akar, Ö., & Görmüş, E. T., (2019). Göktürk-2 ve Hyperion EO-1 uydu görüntülerinden rastgele orman sınıflandırıcısı ve destek vektör makineleri ile arazi kullanım haritalarının üretilmesi. Geomatik, 4(1), 68-81. https://doi.org/10.29128/geomatik.476668
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  • Breiman , L. (1996). Bagging predictors. Machine Learning, 24 (2), 123–140.
  • Breiman, L. (2001). Random forests. Machine Learning, 45 (1), 5–32.
  • Brennan, C., & Hoene, C. (2003). Research brief on Americas cities: Demographic change in small cities, 1990- 2000. National League of Cities. Washington, DC.
  • Burbridge, S., & Zhang, Y. Z. Y. (2003). A neural network based approach to detecting urban land cover changes using Landsat TM and IKONOS imagery. 2003 2nd GRSS/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas (pp. 157-161). IEEE.
  • Çakır, F. S. (2020). Yapay sinir ağları matlab kodları ve matlab toolbox çözümleri. Nobel Yayıncılık, Ankara.
  • Çalışkan, O.Ç. ve Tezer, A. (2018). Türkiye kentleşmesinin çok boyutlu sürdürülemezliğinde yeni bir yol arayışı: orta ölçekli kentler üzerinden kır-kent dayanışma ağları, Planlama (Ek 1): 73-90. https://doi.org/10.14744/planlama.2018.66376
  • Canpolat, F. A. (2019). Tunceli kentinin nüfus özellikleri. Uluslararası Bilimsel Araştırmalar Dergisi (IBAD), 4(2), 183- 200. https://doi.org/10.21733/ibad.537457
  • Chapelle, O., Haffner, P., & Vapnik, V. N. (1999). Support vector machines for histogram-based image classification. IEEE transactions on Neural Networks, 10(5), 1055-1064. https://doi.org/10.1109/72.788646
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  • Dadashpoor, H., Azizi, P., Maghadasi, M. (2019) Analyzing spatial patterns, driving forces and predicting future growth scenarios for supporting sustainable urban growth: evidence from Tabriz metropolitan area, Iran, Sustainable Cities and Society, 47, 1-32. https://doi.org/10.1016/j.scs.2019.101502
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Dağlık alanlarda makine öğrenmesi ile kentsel büyümeye uygun alanların belirlenmesi: Tunceli kenti örneği

Yıl 2022, Sayı: 47, 210 - 232, 30.09.2022

Fethi Ahmet Canpolat

https://doi.org/10.32003/igge.1119297

Öz

Dünyanın birçok bölgesinde kentleşme, insanın mekâna olan müdahalesini arttıran en önemli tetikleyici unsurlardan birine dönüşmüş durumdadır. Dolayısıyla kentleşme sürecinin, diğer beşeri faaliyetlere göre yönetilmesi ve planlanması öncelik arz etmektedir. Kentleşme konusundaki çalışmalar ağırlıklı olarak büyük şehirler üzerinde yoğunlaşmasına rağmen, küçük ölçekli kentler hem nüfus miktarı hem de sayı açısından, dünya genelinde oldukça fazladır. Ayrıca küçük kentler, kalkınmanın ulusal ve bölgesel düzeyde daha homojen dağılmasında etkili olabilecek alanlardır. Ancak bu yerleşmelerin büyümesinde, situasyonu, hinterlandı ve potansiyelin kullanılamaması gibi çeşitli sınırlılıklar engel oluşturabilmektedir. Tunceli kenti de küçük ölçekli ve hem doğal hem de beşeri faktörler tarafından büyümesinde kısıtlılıkları olan bir yerleşmedir. Bu çalışmada makine öğrenmesi algoritmalarından “destek vektör makineleri”, “yapay sinir ağları” ve “rastgele orman” modelleri kullanılarak kentsel büyümeye uygun alanlar tespit edilmiştir. Kentte içe doğru yığılmalı büyüme ile periferik büyümeye elverişli alanların öncelikli büyümeye uygun olduğu (12 km2) tespit edilmiştir. Kullanılan modellerde, %90’ın üzerinde tahmin doğruluğuna ulaşılmasına rağmen, sonuçların mekânsal karşılığı açısından en iyi sonuçların sırasıyla “rastgele orman”, “yapay sinir ağları” ve son olarak “destek vektör makineleri” modelinde elde edilmiştir.

Anahtar Kelimeler

Tunceli kentsel büyüme destek vektör makineleri yapay sinir ağları rastgele orman

Kaynakça

  • Akar, Ö. & Güngör, O. (2012). Rastgele orman algoritması kullanılarak çok bantlı görüntülerin sınıflandırılması. Jeodezi ve Jeoinformasyon Dergisi , (106), 139-146 . https://doi.org/10.9733/jgg.241212.1t
  • Akar, Ö., & Görmüş, E. T., (2019). Göktürk-2 ve Hyperion EO-1 uydu görüntülerinden rastgele orman sınıflandırıcısı ve destek vektör makineleri ile arazi kullanım haritalarının üretilmesi. Geomatik, 4(1), 68-81. https://doi.org/10.29128/geomatik.476668
  • Alkheder, S., (1999). Urban growth simulation using remote sensing imaginary and neural networks, retrieved from: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.125.8120&rep=rep1&type=pdf
  • Aslan, S. (2016). Şehir içi arazi kullanım yönünden Tunceli. (Yüksek Lisans Tezi, Ankara Üniversitesi, Sosyal Bilimler Enstitüsü, Ankara). s.
  • Aydın, M., & Çelik, E. (2013). Destek vektör makineleri ve yapay sinir ağları kullanarak türkiye’deki tehlikeli hava durumlarının uydu görüntüleri ile erken tespiti. 21st Signal Processing and Communications Applications Conference (SIU 2013) : North Cyprus Turkish Republic, 24 - 26 April 2013, Haspolat.
  • Beck M. W. (2018). Neuralnettools: Visualization and analysis tools for neural networks. Journal of statistical software, 85(11), 1–20. https://doi.org/10.18637/jss.v085.i11
  • Breiman , L. (1996). Bagging predictors. Machine Learning, 24 (2), 123–140.
  • Breiman, L. (2001). Random forests. Machine Learning, 45 (1), 5–32.
  • Brennan, C., & Hoene, C. (2003). Research brief on Americas cities: Demographic change in small cities, 1990- 2000. National League of Cities. Washington, DC.
  • Burbridge, S., & Zhang, Y. Z. Y. (2003). A neural network based approach to detecting urban land cover changes using Landsat TM and IKONOS imagery. 2003 2nd GRSS/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas (pp. 157-161). IEEE.
  • Çakır, F. S. (2020). Yapay sinir ağları matlab kodları ve matlab toolbox çözümleri. Nobel Yayıncılık, Ankara.
  • Çalışkan, O.Ç. ve Tezer, A. (2018). Türkiye kentleşmesinin çok boyutlu sürdürülemezliğinde yeni bir yol arayışı: orta ölçekli kentler üzerinden kır-kent dayanışma ağları, Planlama (Ek 1): 73-90. https://doi.org/10.14744/planlama.2018.66376
  • Canpolat, F. A. (2019). Tunceli kentinin nüfus özellikleri. Uluslararası Bilimsel Araştırmalar Dergisi (IBAD), 4(2), 183- 200. https://doi.org/10.21733/ibad.537457
  • Chapelle, O., Haffner, P., & Vapnik, V. N. (1999). Support vector machines for histogram-based image classification. IEEE transactions on Neural Networks, 10(5), 1055-1064. https://doi.org/10.1109/72.788646
  • Chen, J., Li, M., Wang, W. (2012). Statistical uncertainty estimation using random forests and its application to drought forecast. Mathematical Problems in Engineering, https://doi.org/10.1155/2012/915053
  • Cheng J., & Masser, I. (2003). Understanding urban growth system: Theories and methods. In 8th International Conference on Computers in Urban Planning and Urban Management, Sendai City, Japan, pages229–237.
  • Church, R. L. (2002). Geographical information systems and location science. Computers & Operations Research, 29(6), 541-562. https://doi.org/10.1016/S0305-0548(99)00104-5
  • Cohen, B. (2004). Urban growth in developing countries: a review of current trends and a caution regarding existing forecasts, World development, 32(1), 23-51. https://doi.org/10.1016/j.worlddev.2003.04.008
  • Cuhls, K. (2003). From forecasting to foresight processes—new participative foresight activities in Germany. Journal of forecasting, 22(2‐3), 93-111. https://doi.org/10.1002/for.848
  • Dadashpoor, H., Azizi, P., Maghadasi, M. (2019) Analyzing spatial patterns, driving forces and predicting future growth scenarios for supporting sustainable urban growth: evidence from Tabriz metropolitan area, Iran, Sustainable Cities and Society, 47, 1-32. https://doi.org/10.1016/j.scs.2019.101502
  • Erdem, F. , Derinpınar, M. A. , Nasırzadehdızajı, R. , Oy, S. , Şeker, D. Z. & Bayram, B. (2018). Rastgele orman yöntemi kullanılarak kıyı çizgisi çıkarımı İstanbul örneği, Geomatik, 3(2), 100-107. https://doi.org/10.29128/geomatik.362179
  • Esen, F. (2021). Jeomorfolojik özelliklerin Tunceli şehrinin gelişimine etkileri. Jeomorfolojik Araştırmalar Dergisi , (7) , 109-131 . https://10.46453/jader.948540
  • ESRI (2022-a). How slope works. 10 Şubat 2022 tarihinde https://desktop.arcgis.com/en/arcmap/10.3/tools/spatial-analyst-toolbox/how-aspect-works.htm, adresinden edinilmiştir.
  • ESRI (2022-b). How aspect works. 10 Şubat 2022 tarihinde https://pro.arcgis.com/en/pro-app/2.8/tool- reference/spatial-analyst/how-aspect-works.htm, adresinden edinilmiştir.
  • ESRI (2022-c). Understanding euclidean distance analysis, 10 Şubat 2022 tarihinde https://desktop.arcgis.com/en/arcmap/latest/tools/spatial-analyst-toolbox/understanding-euclidean- distance-analysis.htm, adresinden edinilmiştir.
  • Fridemann, J. R. (1986). The world city hypothesis: development and change. Urban Studies, 23(2), 59-137.
  • Frimpong, B. F., & Molkenthin, F. (2021). Tracking urban expansion using random forests for the classification of landsat imagery (1986–2015) and predicting urban/built-up areas for 2025: A Study of the Kumasi Metropolis, Ghana. Land, 10(1), 44. https://doi.org/10.3390/land10010044
  • GEOFABRIK. (2022). Open street map, 10 Şubat 2022 tarihinde https://download.geofabrik.de/europe/turkey.html, adresinden edinilmiştir.
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  • İzmen, Ü. (2014). Bölgesel kalkınmada yerel dinamikler: Tunceli modeli ve 2023 senaryoları, Fam Yayınları, İstanbul, ISBN: 978-605-6487-90-3
  • Jun, M. J. (2021). A comparison of a gradient boosting decision tree, random forests, and artificial neural networks to model urban land use changes: The case of the Seoul metropolitan area. International Journal of Geographical Information Science, 35(11), 2149-2167. https://doi.org/10.1080/13658816.2021.1887490
  • Karadağ, A., & Koçman, A. (2007). Coğrafi çevre bileşenlerinin kentsel gelişim süreci üzerine etkileri: Ödemiş (İzmir) örneği, Ege Coğrafya Dergisi, 16(1-2), 3-16.
  • Karimi, F., Sultana, S., Babakan, A. S., & Suthaharan, S. (2019). An enhanced support vector machine model for urban expansion prediction. Computers, Environment and Urban Systems, 75, 61-75. https://doi.org/10.1016/j.compenvurbsys.2019.01.001
  • Kavzoğlu, T., & Çölkesen, İ. (2010). Destek vektör makineleri ile uydu görüntülerinin sınıflandırılmasında kernel fonksiyonlarının etkilerinin incelenmesi, Harita Dergisi, 144(7), 73-82.
  • Li, C., Li, J., & Wu, J. (2018). What drives urban growth in China? A multi-scale comparative analysis. Applied Geography, 98, 43–51. https://doi.org/10.1016/j.apgeog.2018.07.002
  • Liaw, A. ve Wiener, M. (2002). Classification and regression by randomForest. R news, 2(3), 18-22. https://cogns.northwestern.edu/cbmg/LiawAndWiener2002.pdf
  • Liong, S. Y., & Sivapragasam, C. (2002). Flood stage forecasting with support vector machines, Journal of the American Water Resources Association, 38(1), 173-186. https://doi.org/10.1111/j.1752-1688.2002.tb01544.x
  • Liu, X. ve Lathrop R. G. (2002) Urban change detection based on an artificial neural network, International Journal of Remote Sensing, 23:12, 2513-2518, https://doi.org/10.1080/01431160110097240
  • Malczewski, J. (2004). GIS-based land-use suitability analysis: a critical overview. Progress in planning, 62(1), 3- 65. https://doi.org/10.1016/j.progress.2003.09.002
  • Mattivi, P., Franci, F., Lambertini, A., & Bitelli, G. (2019). TWI computation: a comparison of different open source GISs. Open Geospatial Data, Software and Standards, 4(1), 1-12. https://doi.org/10.1186/s40965-019-0066-y
  • Mendiratta, P., & Gedam, S. (2018). Assessment of urban growth dynamics in Mumbai Metropolitan Region, India using object-based image analysis for medium resolution data. Applied Geography, 98, 110–120. https://doi.org/10.1016/j.apgeog.2018.05.017
  • Meyer, D. (2001). Support vector machines. R News, 1(3), 23-26. https://cran.r- project.org/web/packages/e1071/vignettes/svmdoc.pdf
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  • Özdemir, M. A. (1996). Türkiye’de büyük yerleşme alanlarının seçiminde jeomorfolojik esaslar. Fırat Üniversitesi Sosyal Bilimler Dergisi, 8(2), 209-222.
  • Pal, M., & Mather, P. M. (2005). Support vector machines for classification in remote sensing. International Journal of Remote Sensing, 26(5), 1007-1011. https://doi.org/10.1080/01431160512331314083
  • Pal, M., (2005) Random forest classifier for remote sensing classification. International Journal of Remote Sensing, 26:1, 217-222, https://doi.org/10.1080/01431160412331269698
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  • Üzmez, U. (2012). Türkiye’de orta ölçekli kentsel alanlar sorununa çözüm arayışları: Zonguldak örneği. Gazi Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 14(2), 127-158.
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Toplam 72 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Beşeri Coğrafya
Bölüm ARAŞTIRMA MAKALESİ
Yazarlar

Fethi Ahmet Canpolat 0000-0002-6084-7735

Yayımlanma Tarihi 30 Eylül 2022
Yayımlandığı Sayı Yıl 2022 Sayı: 47

Kaynak Göster

APA Canpolat, F. A. (2022). The use of machine learning to identify suitable areas for urban growth in mountainous areas: Tunceli city example. Lnternational Journal of Geography and Geography Education(47), 210-232. https://doi.org/10.32003/igge.1119297
AMA Canpolat FA. The use of machine learning to identify suitable areas for urban growth in mountainous areas: Tunceli city example. IGGE. Eylül 2022;(47):210-232. doi:10.32003/igge.1119297
Chicago Canpolat, Fethi Ahmet. “The Use of Machine Learning to Identify Suitable Areas for Urban Growth in Mountainous Areas: Tunceli City Example”. Lnternational Journal of Geography and Geography Education, sy. 47 (Eylül 2022): 210-32. https://doi.org/10.32003/igge.1119297.
EndNote Canpolat FA (01 Eylül 2022) The use of machine learning to identify suitable areas for urban growth in mountainous areas: Tunceli city example. lnternational Journal of Geography and Geography Education 47 210–232.
IEEE F. A. Canpolat, “The use of machine learning to identify suitable areas for urban growth in mountainous areas: Tunceli city example”, IGGE, sy. 47, ss. 210–232, Eylül 2022, doi: 10.32003/igge.1119297.
ISNAD Canpolat, Fethi Ahmet. “The Use of Machine Learning to Identify Suitable Areas for Urban Growth in Mountainous Areas: Tunceli City Example”. lnternational Journal of Geography and Geography Education 47 (Eylül 2022), 210-232. https://doi.org/10.32003/igge.1119297.
JAMA Canpolat FA. The use of machine learning to identify suitable areas for urban growth in mountainous areas: Tunceli city example. IGGE. 2022;:210–232.
MLA Canpolat, Fethi Ahmet. “The Use of Machine Learning to Identify Suitable Areas for Urban Growth in Mountainous Areas: Tunceli City Example”. Lnternational Journal of Geography and Geography Education, sy. 47, 2022, ss. 210-32, doi:10.32003/igge.1119297.
Vancouver Canpolat FA. The use of machine learning to identify suitable areas for urban growth in mountainous areas: Tunceli city example. IGGE. 2022(47):210-32.