Yapay Sinir Ağları ile Kentsel Büyümenin Modellenmesi - Kırklareli Örneği

Yıl 2023, Cilt: 2 Sayı: 1, 17 - 30, 30.06.2023

Azem Kuru

Öz

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

Anahtar Kelimeler

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

Destekleyen Kurum

Yok

Proje Numarası

Yok

Teşekkür

Yok

Urban Growth Prediction with Artificial Neural Networks – Kırklareli Case Study

Öz

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.

Anahtar Kelimeler

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

Proje Numarası

Yok

Kaynakça

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