Application of artificial neural networks in modeling urban physical development (Case study: Rasht city)

Subject Areas : Urban and Regional Planning Studies

tala abedi ¹ , golamreza miri ² , parviz rezaei ³ , reza zarei ⁴

1 - PhD student in Geography and Urban Planning, Astara Branch, Islamic Azad University, Astara, Iran
2 - Assistant Professor, Department of Geography and Urban Planning, Zahedan Branch, Islamic Azad University, Zahedan, Iran
3 - Associate Professor, Department of Geography, Rasht Branch, Islamic Azad University, Rasht, Iran
4 - Assistant Professor, Department of Statistics, Gilan University, Rasht, Iran

Received: 2022-11-03 Accepted : 2022-11-27 Published : 2024-08-22

Keywords: Artificial Neural Network, Modeling, Rasht City, Physical development of the city,

Abstract :

Introduction: The physical development of cities is increasing day by day. Correct management of this development from various aspects is among the important issues that must be considered. There are many methods for predicting and determining the direction of urban development, one of these methods for determining suitable areas is the method based on neural networks.The purpose of the research: The purpose of this research is to model the development of the city of Rasht in the last 20 years and predict the directions of development of this city until 2032.Research methodology :By using ETM+ Landsat 7 and 8 satellite images of 2002, 2012 and 2021 of Rasht city and with GIS software, images with suitable band composition are prepared and then the images are classified using Multi Layer Perceptron Artificial Neural Network (MLP) method. The indicators considered for the neighborhood model of urban areas are the distance from urban points, the distance to the central areas of the city, and the distance to the main streets and roads.The geographical scope of the research:Rasht city, the capital of Gilan province, is located at 49 degrees 35 minutes 45 seconds east longitude and 37 degrees 16 minutes 30 seconds north latitude from the Greenwich meridian, and its area is about 10,240 hectares.Findings and discussion:In this model, in the training mode of the first stage (input, applying 4 indexes on the images of 2002), the network performed 104 iterations, and the lowest error rate, which is evaluated by the crossentropy criterion, was equal to 0.058526 in the 98th iteration. In the second step, the input of the model was to apply 4 indicators on the images of 2012, and the lowest error rate was evaluated as 0.076657.Results :In total, the model has been able to predict the development of Rasht city in 2012, 95.9% and for 2021, 93.8%, which can be acceptable. The model error in this first part was 1.4% and in the second part was 2.6%. By examining the 20-year period of physical development, the development directions of Rasht city in 2032 were predicted.