Nowadays, urban sprawl phenomenon have been seen in many of cities in the developing and developed countries. Urban sprawl is considered as a particular kind of urban growth which comes up with a lot of negative effects. Thus, monitoring, analyzing and modeling of this phenomenon seem to be unavoidable. This paper assess urban sprawl in Tehran Metropolis as the capital of Iran and models urban sprawl in this mega city utilizing artificial neural networks and adaptive neuro-based fuzzy inference system methods with remote sensing data and geospatial information systems spatial analyses and modeling capabilities to simulate Tehran urban growth. The results confirm that this city has experienced sprawl and sprawl has an increasing rate. Three Landsat imageries from TM and ETM? sensors taken in 1988, 1999 and 2010 and seven predictor variables include distance to road, distance to green space, slope, elevation, distance to fault, distance to developed area and number of urban cells in 3 by 3 neighborhoods have been used for urban sprawl assessment and modeling. Relative operating characteristics (ROC) and sensitivity analyses have been used to evaluate simulation results. In this research, two evaluation steps have been implemented using ROC and on the both of them, ANFIS presented the best performance vs two different proposed ANN structures.

ABSTRACT:Land use activity is a major issue and challenge for town and country planners. Modelling and managing urban growth is a complex problem. Cities are now recognized as complex, non-linear and dynamic process systems. The design of a system that can handle these complexities is a challenging prospect. Local governments that implement urban growth models need to estimate the amount of urban land required in the future given anticipated growth of housing, business, recreation and other urban uses within the boundary. There are so many negative implications related with the type of inappropriate urban development such as increased traffic and demand for mobility, reduced landscape attractively, land use fragmentation, loss of biodiversity and alterations of the hydrological cycle. The aim of this study is to use the Artificial Neural Network (ANN) to make a powerful tool for simulating urban growth patterns. Our study area is Sanandaj city located in the west of Iran. Landsat imageries acquired at 2000 and 2006 are used. Dataset were used include distance to principle roads, distance to residential areas, elevation, slope, distance to green spaces and distance to region centers. In this study an appropriate methodology for urban growth modelling using satellite remotely sensed data is presented and evaluated. Percent Correct Match (PCM) and Figure of Merit were used to evaluate ANN results.

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.International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-1/W3, 2013 SMPR 2013, 5 -8 October 2013 This contribution has been peer-reviewed. The peer-review was conducted on the basis of the abstract. 493In 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 imag...

Urban sprawl is a common phenomenon in developed and developing countries. Population growth and immigration to cities are the most important reason to such urban expansions. Out layer growths which in the most cases results sprawl has so many attractions such as low rate of crime, lower costs of living and clean air. These are probably the reasons to rapid increase of this phenomenon. Urban sprawl is a kind of growth in cities which have derived so many negative impacts such as agriculture and natural land loss, environmental pollution, high rate of travel time and costs, high rate of energy consuming and etc.,. Thus, analyzing and monitoring urban area is a key task for urban planning to inform about urban growth. Numerous researches have attempted to characterize and explain urban sprawl. In this study, we implemented Shannon Entropy for assessments of urban sprawl. The case study is Tehran Metropolis which has experienced so fast urbanization and population growth in the recent decades.

Background: Global urban population has increased from 22.9 % in 1985 to 47 % in 2010. In Iran, population living in urban areas has consistently increased from about 31 % in 1956 to 68.4 % in 2006. Urban growth as one of the results of rapid population growth, results lots of problems. Thus, monitoring and modelling of the urban expansion is necessary. Methods: In this research, a novel Adaptive Neuro Fuzzy Inference System (ANFIS)-based methodology has been developed for urban growth modeling, as well as interpreting the relationship between the drivers of urbanization. Then, ANFIS results were compared with those achieved by both ANN and Logistic Regression (LR)-based methodologies using Percent Area Match quantity and Percent Area Match location to assess model goodness of fit. Results: The proposed ANFIS model which takes the advantages of using neural networks and fuzzy logic at the same time, had the best performances among the three implemented models. It was able to identify important factors in the development and their relationship and influence on the growth of the city. Conclusions: The research aim is to find a computational based method which can effectively capture, analyse and model the complex nature of spatial phenomenon like urban growth. The proposed ANFIS method due to its structure is able to deals with nonlinear phenomenon. Integration of Remote sensing data, GIS tools and also, computational based method provide us an effective, reliable and also, scientific methods for monitoring, analysing and modeling of environmental phenomenon.

Land use activity is a major issue and challenge for town and country planners. Urban planners must be able to allocate urban land area to different applications with a special focus on the role and function of the city, its economy, and the ability to simulate the effect of user interaction with each other. Continuing migration of rural population to cities and population increases has caused many problems of today's cities including the expansion of urban areas, lack of infrastructure and urban services as well as environmental pollution. Local governments that implement urban growth boundaries need to estimate the amount of urban land required in the future given anticipated growth of housing, business, recreation and other urban activities. Urban growth is a complex process that encounters a number of sophisticated parameters that interact to produce the urban growth pattern. Urban growth modelling aims to understand the dynamic processes. Therefore, interpretability of models is becoming increasingly important. Different approaches have been applied in spatial modelling. In this study, Particle Swarm Optimization (PSO) has been used for modelling of urban growth in Qazvin city area (Iran) during 2005 to 2011. Landsat imageries, taken in 2005 and 2011 have been used in the study. Main parameters in this study are distance to residential area, distance to industrial area, slope, accessibility, land price and number of urban cell in a 3*3 neighbourhood. Figure of Merit and Kappa statistics have been used for estimating accuracy of the proposed model.

Straipsnyje yra analizuojamos aplinkos apsaugos politikos ištakos ir jos raida įvairiais Lietuvos istoriniais laikotarpiais. Darbe yra aptariamos Pirmosios Lietuvos Respublikos aplinkos apsaugos politikos užuomazgos, diskutuojami įvairūs požiūriai ar tai buvo politikos pradžia, ar tik klasikinė aplinkosauga. Taip pat yra vertinamas sovietinio valdymo laikotarpis, kai buvo priimtas Gamtos apsaugos įstatymas, kuris pradėjo teisiškai reglamentuoti aplinkosaugą ir nustatė pirmus svarbiausius aplinkos apsaugos valdymo principus. Remiantis aplinkosaugos specialistų nuomone yra aptariami pirmieji atkurtos Lietuvos Respublikos nepriklausomybės metai, kai teisiškai buvo nustatyti svarbiausi aplinkos apsaugos valdymo politikos principai. Vėlesniais metais aplinkos apsaugos politika buvo formuojama ne tik teisės aktų pagalba, bet patvirtinus Valstybinę aplinkos apsaugos strategiją bei Nacionalinę aplinkos apsaugos strategiją. Šios strategijos suformavo svarbiausius Lietuvos aplinkos apsaugos politikos principus. Straipsnyje taip pat yra aptariama aplinkos apsaugos politikos samprata, pateikiami skirtingi į ją požiūriai.