A spatio-temporal urban expansion modeling a case study Teheran metropolis, Iran

During the past decades, urban growth has been accelerating with the massive immigration of population to cities. Urban population in the world was estimated as 2.9 billion in 2000 and predicted to reach 5.0 billion in 2030. Rapid urbanization and population growth have been a common phenomenon, especially in the developing countries such as Iran. Rapid population growth, environmental changes and improper land use planning practices in the past decades have resulted in environmental deterioration, haphazard landscape development and stress on the ecosystem structure, housing shortages, insufficient infrastructure, and increasing urban climatological and ecological problems. In this study, urban sprawl assessment was implemented using Shannon entropy and then, Artificial Neural Network (ANN) has been adopted for modeling urban growth. Our case study is Tehran Metropolis, capital of Iran. Landsat imageries acquired in 1988, 1999 and 2010 are used. According to the results of sprawl assessment for this city, this city has experienced sprawl between 1988 to 2010. Dataset include distance to roads, distance to green spaces, distance to developed area, slope, number of urban cells in a 3 by 3 neighborhood, distance to fault and elevation. Relative operating characteristic (ROC) method have been used to evaluate the accuracy and performance of the model. The obtained ROC equal to 0.8366

Urban Growth Modelling with Artificial Neural Network and Logistic Regression. Case Study: Sanandaj City, Iran

Cities have shown remarkable growth due to attraction, economic, social and facilities centralization in the past few decades. Population and urban expansion especially in developing countries, led to lack of resources, land use change from appropriate agricultural land to urban land use and marginalization. Under these circumstances, land use activity is a major issue and challenge for town and country planners. Different approaches have been attempted in urban expansion modelling. Artificial Neural network (ANN) models are among knowledge-based models which have been used for urban growth modelling. ANNs are powerful tools that use a machine learning approach to quantify and model complex behaviour and patterns. In this research, ANN and logistic regression have been employed for interpreting urban growth modelling. Our case study is Sanandaj city and we used Landsat TM and ETM+ imageries acquired at 2000 and 2006. The dataset used includes distance to main roads, distance to the residence region, elevation, slope, and distance to green space. Percent Area Match (PAM) obtained from modelling of these changes with ANN is equal to 90.47% and the accuracy achieved for urban growth modelling with Logistic Regression (LR) is equal to 88.91%. Percent Correct Match (PCM) and Figure of Merit for ANN method were 91.33% and 59.07% and then for LR were 90.84% and 57.07%, respectively

A SPATIO-TEMPORAL URBAN EXPANSION MODELING A CASE STUDY TEHRAN METROPOLIS, IRAN

During the past decades, urban growth has been accelerating with the massive immigration of population to cities. Urban population in the world was estimated as 2.9 billion in 2000 and predicted to reach 5.0 billion in 2030. Rapid urbanization and population growth have been a common phenomenon, especially in the developing countries such as Iran. Rapid population growth, environmental changes and improper land use planning practices in the past decades have resulted in environmental deterioration, haphazard landscape development and stress on the ecosystem structure, housing shortages, insufficient infrastructure, and increasing urban climatological and ecological problems. In this study, urban sprawl assessment was implemented using Shannon entropy and then, Artificial Neural Network (ANN) has been adopted for modeling urban growth. Our case study is Tehran Metropolis, capital of Iran. Landsat imageries acquired in 1988, 1999 and 2010 are used. According to the results of sprawl assessment for this city, this city has experienced sprawl between 1988 to 2010. Dataset include distance to roads, distance to green spaces, distance to developed area, slope, number of urban cells in a 3 by 3 neighborhood, distance to fault and elevation. Relative operating characteristic (ROC) method have been used to evaluate the accuracy and performance of the model. The obtained ROC equal to 0.8366