"Evaluating the Potential and Optimal Urban Growth Pattern by Using of Neural Network Algorithms (Case Study: Zanjan City)"
Abolfazl Ghanbari
1
(
Academic Member/University of Tabriz
)
samaneh bagheri
2
(
Tabriz University
)
Mohammad Soorghali
3
(
Ph.D Student, , University of Tabriz
)
Keywords: Neural network and Zanjan, optimal urban growth model, potential evaluation, Physical growth of the city,
Abstract :
The dynamic phenomenon of urban physical growth is indicative of population increase, economic development, and the rising importance of cities. The potential and optimal growth pattern of cities are among the crucial factors influencing sustainable development and efficiency in these centers. Accurate and effective assessment of these potentials requires the use of advanced and precise methods. Therefore, the aim of the present research is to evaluate the potential and optimal growth pattern of the city, with a focus on Zanjan, using neural network algorithms.This research is applied and developmental in terms of its objective, and in the way data is collected, it is descriptive-analytical. Initially, parameters influencing the growth of Zanjan were identified, and neural network models were designed. Subsequently, using available data and patterns of past growth, neural networks were trained to predict the future growth and development of the city. The data used in this research includes land use layers of Zanjan and Landsat 8 satellite imagery, obtained from the Zanjan Municipality and the United States Geological Survey (USGS), respectively.The results indicate that Zanjan has high potential for physical growth in its western and eastern parts, characterized by factors such as elevation and slope meeting standard conditions. However, the northern and southern parts, due to obstacles like high elevation, slopes exceeding 5%, and the presence of orchards in the southern section, lack potential for development. Evaluating the potential and optimal growth pattern of Zanjan using neural networks can assist urban authorities and decision-makers in adopting suitable strategies for sustainable development and greater efficiency in the city. By leveraging historical and current data, they can predict their future and undertake proper planning for sustainable and optimal urban development.
