Volume XL-1/W3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W3, 493-498, 2013
https://doi.org/10.5194/isprsarchives-XL-1-W3-493-2013
© 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, 493-498, 2013
https://doi.org/10.5194/isprsarchives-XL-1-W3-493-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

  29 Oct 2013

29 Oct 2013

URBAN GROWTH MODELING USING ANFIS ALGORITHM: A CASE STUDY FOR SANANDAJ CITY, IRAN

S. Mohammady1, M. R. Delavar2, and B. C. Pijanowski3 S. Mohammady et al.
  • 1Dept. of Surveying and Geomatics Eng., College of Eng., University of Tehran, Tehran, Iran
  • 2Center of Excellence in Geomatic Eng. in Disaster Management, Dept. of Surveying and Geomatic Eng., College of Eng., University of Tehran, Tehran, Iran
  • 3Dept. of Forestry and Natural Resources, College of Agriculture, Purdue University, West Lafayette, IN 47907, USA

Keywords: Urban Growth, Modelling, ANFIS, GIS

Abstract. Global urban population has increased from 22.9% in 1985 to 47% in 2010. In spite of the tendency for urbanization worldwide, only about 2% of Earth's land surface is covered by cities. Urban population in Iran is increasing due to social and economic development. The proportion of the population living in Iran urban areas has consistently increased from about 31% in 1956 to 68.4% in 2006. Migration of the rural population to cities and population growth in cities have caused many problems, such as irregular growth of cities, improper placement of infrastructure and urban services. Air and environmental pollution, resource degradation and insufficient infrastructure, are the results of poor urban planning that have negative impact on the environment or livelihoods of people living in cities. These issues are a consequence of improper land use planning.

Models have been employed to assist in our understanding of relations between land use and its subsequent effects. Different models for urban growth modeling have been developed. Methods from computational intelligence have made great contributions in all specific application domains and hybrid algorithms research as a part of them has become a big trend in computational intelligence. Artificial Neural Network (ANN) has the capability to deal with imprecise data by training, while fuzzy logic can deal with the uncertainty of human cognition. ANN learns from scratch by adjusting the interconnections between layers and Fuzzy Inference Systems (FIS) is a popular computing framework based on the concept of fuzzy set theory, fuzzy logic, and fuzzy reasoning. Fuzzy logic has many advantages such as flexibility and at the other sides, one of the biggest problems in fuzzy logic application is the location and shape and of membership function for each fuzzy variable which is generally being solved by trial and error method. In contrast, numerical computation and learning are the advantages of neural network, however, it is not easy to obtain the optimal structure. Since, in this type of fuzzy logic, neural network has been used, therefore, by using a learning algorithm the parameters have been changed until reach the optimal solution. Adaptive Neuro Fuzzy Inference System (ANFIS) computing due to ability to understand nonlinear structures is a popular framework for solving complex problems. Fusion of ANN and FIS has attracted the growing interest of researchers in various scientific and engineering areas due to the growing need of adaptive intelligent systems to solve the real world problems.

In this research, an ANFIS method has been developed for modeling land use change and interpreting the relationship between the drivers of urbanization. Our study area is the city of Sanandaj located in the west of Iran. Landsat images acquired in 2000 and 2006 have been used for model development and calibration. The parameters used in this study include distance to major roads, distance to residential regions, elevation, number of urban pixels in a 3 by 3 neighborhood and distance to green space. Percent Correct Match (PCM) and Figure of Merit were used to assess model goodness of fit were 93.77% and 64.30%, respectively.