Volume XL-1/W3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W3, 231-234, 2013
https://doi.org/10.5194/isprsarchives-XL-1-W3-231-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W3, 231-234, 2013
https://doi.org/10.5194/isprsarchives-XL-1-W3-231-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

  24 Sep 2013

24 Sep 2013

Modeling of urban growth using cellular automata (CA) optimized by Particle Swarm Optimization (PSO)

M. H. Khalilnia1, T. Ghaemirad2, and R. A. Abbaspour1 M. H. Khalilnia et al.
  • 1Department of Surveying Eng., College of Engineering, University of Tehran, Iran
  • 2Faculty of Geodesy and Geomatics Eng., K.N.T. University of Technology, Iran

Keywords: Urban Growth, Cellular Automata, Particle Swarm Optimization, Logistic Regression

Abstract. In this paper, two satellite images of Tehran, the capital city of Iran, which were taken by TM and ETM+ for years 1988 and 2010 are used as the base information layers to study the changes in urban patterns of this metropolis. The patterns of urban growth for the city of Tehran are extracted in a period of twelve years using cellular automata setting the logistic regression functions as transition functions. Furthermore, the weighting coefficients of parameters affecting the urban growth, i.e. distance from urban centers, distance from rural centers, distance from agricultural centers, and neighborhood effects were selected using PSO. In order to evaluate the results of the prediction, the percent correct match index is calculated. According to the results, by combining optimization techniques with cellular automata model, the urban growth patterns can be predicted with accuracy up to 75 %.