1- Islamic Azad university
2- Kharazmi University of Tehran
3- Kharazmi University of Tehran , hedjazizadeh@yahoo.com
2- Kharazmi University of Tehran
3- Kharazmi University of Tehran , hedjazizadeh@yahoo.com
Abstract: (1449 Views)
Using urban growth and development modeling, it is possible to draw a development trend appropriate to the city's position according to environmental and natural factors and population attraction. The purpose of this study is to represent a model of urban development in Shushtar that can be used as a felicitous tool to analyze the complex processes of urban development. To achieve this goal, two databases consist of urban land use maps for educational, medical, habitation, etc and Landsat satellite images for major land uses such as rivers, barren areas, forests, etc were used by GIS and MATLAB software environment in three time periods 1991, 2004 and 2014. Existing urban land use maps were updated by using Landsat satellite imagery after digitization. Then the effective parameters in urban development were entered as inputs with the adaptive neuro-fuzzy inference algorithm (ANFIS). in order to evaluate the performance of the proposed method, training for 1991 and 2004 was performed. the result of urban development forecasting using the algorithm was compared with the current situation in 2014. The results are very close to reality and with an accuracy of 93.7%. The land use change map, which is the result of the change detection process, can be prepared based on multi-time remote sensing images and combined with urban user maps, and the relevant consequences examined. The use of intelligent algorithms in this research has allowed us to execute modeling with high accuracy. The results are satisfactory and this development was predicted for the coming years.
Type of Study: Research |
Subject:
Geography and Urban Planning
Received: 2022/12/23 | Accepted: 2023/02/22
Received: 2022/12/23 | Accepted: 2023/02/22
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Journal of Applied researches in Geographical Sciences are fully compliant with open access mandates, by publishing its articles under Creative Commons Attribution 4.0 International License (CC BY 4.0).
