urban growth modeling using integrated cellular automata and gravitational search algorithm (case study: shiraz city, iran)

Cities are growing and encountering many changes over time due to population growth and migration. identification and detection of these changes play important roles in urban management and sustainable development. urban growth models are divided into two main categories: first cellular models which are further divided into experimental, dynamic, and integrated models and second vector models. in this study, an integrated urban growth model is proposed which is a combination of cellular automata and gravitational search algorithm (gsa). it has been implemented on shiraz (iran) to model the urban growth between 1990 and 2000. the proposed integrated model uses gsa to calibrate cellular automata transition rules. the landsat satellite imageries in 1990 and 2000 with digital elevation model (dem) of shiraz are used in this study. five parameters including distances from major roads, urban neighborhood, slope, distances from attraction centers, and distances from parks and other green spaces are considered to be effective in the urban growth modeling. based on the results, kappa coefficient and overall accuracy of the model are 66.54% and 92%, respectively. by using gsa, calibration of cellular automata is facilitated and the proposed integrated model reaches optimal solutions in fewer iterations. the achieved results show that the proposed integrated model can be used for studying urban growth.