modeling urban sprawling of tehran metropolitan area based on pso

the main goal of the present study was to implement a hybrid pattern of cellular automata model and particle swarm optimization algorithm based on tm and etm+ imagery of landsat satellite from 1988 to 2010 for simulating the urban sprawling. in this study, an alternative model was implemented in two ways: the first method was based on two images (1988 and 2010) and the second one was based on three images (1988, 2000 and 2010). parameters such as distance to the nearest urban objects or pixels, distance to the streets and roads, distance to the attraction centers, distance to the city center, distance to the green spaces and parks, the number of urban neighborhood, slope and digital elevation model were considered as effective variables of tehran metropolitan area growth model. the results showed that the hybrid model based on the combination of cellular automata and particles swarm optimization could improve calibration process of cellular automata transformation rules. according to the contingency tables related to the proposed model, the kappa and the overall accuracy indices values were 79.28% and 91.74% for the two-image and 70.11% and 85.46% for the three-image model, respectively