Search

Abstract: This research analyses the suburban expansion in the metropolitan area of Tehran, Iran. A hybrid model consisting of logistic regression model, Markov chain (MC), and cellular automata (CA) was designed to improve the performance of the standard logistic regression model. Environmental and socio-economic variables dealing with urban sprawl were operationalised to create a probability surface of spatiotemporal states of built-up land use for the years 2006, 2016, and 2026. For validation, the model was evaluated by means of relative operating characteristic values for different sets of variables. The approach was calibrated for 2006 by cross comparing of actual and simulated land use maps. The achieved outcomes represent a match of 89% between simulated and actual maps of 2006, which was satisfactory to approve the calibration process. Thereafter, the calibrated hybrid approach was implemented for forthcoming years. Finally, future land use maps for 2016 and 2026 were predicted by means of this hybrid approach. The simulated maps illustrate a new wave of suburban development in the vicinity of Tehran at the western border of the metropolis during the next decades. [Copyright &y& Elsevier]

The main objective of this research is to, first, monitor urban sprawl in the metropolis of Tehran and, second, to assess the CA–Markov model in the simulation of land-use change. Land-use changes generally occur in developing countries through new building construction. The rapid pace of this development has brought forward a number of research activities involving new applications for modelling this phenomenon. In this research, urban sprawl is examined in the urban fringe of Tehran and, subsequently, the CA–Markov model is implemented in order to evaluate the model. The CA–Markov model is a spatially explicit model for land-change modelling, which has not been implemented very frequently to track urban expansion. This model, however, which is an integrated module of both cellular automata and Markov Chain models, predicts forthcoming changes over time, based on the past use of land in the research area. Urban expansion between 1986 and 2006 was simulated through employing the model in the metropolis of Tehran in order to calibrate and customise the model through a training phase. Consequently, the optimal rules to achieve the most accurate results were retrieved and input to simulate future land-use situations, i.e. 2016 and 2026. The achieved results represent a big conversion of green areas and open lands to built-up areas. [ABSTRACT FROM AUTHOR]

Infrastructures play an important role in urbanization and economic activities but are vulnerable. Due to unavailability of accurate subsurface infrastructure maps, ensuring the sustainability and resilience often are poorly recognized. In the current paper a 3D topographical predictive model using distributed geospatial data incorporated with evolutionary gene expression programming (GEP) was developed and applied on a concrete-face rockfill dam (CFRD) in Guilan province- northern to generate spatial variation of the subsurface bedrock topography. The compared proficiency of the GEP model with geostatistical ordinary kriging (OK) using different analytical indexes showed 82.53% accuracy performance and 9.61% improvement in precisely labeled data. The achievements imply that the retrieved GEP model efficiently can provide accurate enough prediction and consequently meliorate the visualization insights linking the natural and engineering concerns. Accordingly, the generated subsurface bedrock model dedicates great information on stability of structures and hydrogeological properties, thus adopting appropriate foundations. [ABSTRACT FROM AUTHOR]