Urban Growth Modeling Using Anfis Algorithm: A Case Study for Sanandaj City, Iran

Mohammady, Sassan; Delavar, Mahmoud Reza; Pijanowski, Bryan C.

doi:10.5194/isprsarchives-xl-1-w3-493-2013

Cited by 6 publications

(4 citation statements)

References 28 publications

(27 reference statements)

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“…One of the major issues in cities intelligent management is the lack of proper and scientific development and as a result urban development in high slope and elevations, destruction of agricultural land, increased infrastructure and utilities costs, environmental deteriorate and natural hazards and the lack of optimum use of land have been encountered (Mohammady et al, 2013).…”

Section: Discussionmentioning

confidence: 99%

Urban Sprawl Monitoring

Mohammady¹,

Delavar²

2015

MAS

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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.

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Section: Discussionmentioning

confidence: 99%

Urban Sprawl Monitoring

Mohammady¹,

Delavar²

2015

MAS

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“…where En is the relative entropy, P i is the probability or percentage of development in the area or P i = X i / n i=1 X i which x i is the density of land development, which is equal to the amount of built-up area divided by the total amount of built-up area in the i th of n total zones [30]- [33], and n is the zone number.…”

Section: Shannon's Entropy and Measuring Urban Sprawlmentioning

confidence: 99%

Evaluation of Urban Sprawl and Land Use/Cover Variation Patterns through Remote Sensing Data: A Case Study in Kabul City, Afghanistan

Ahmadi,

Sahak,

Sahak

2023

EJGEO

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The objective of this study is to utilize geospatial technology and remote sensing data to assess the changes in land use and land cover, as well as urban sprawl, in Kabul city, which serves as the capital of Afghanistan, during the period spanning from 1993 to 2021. Urban sprawl has given rise to unsustainable patterns of urban growth when viewed from social, environmental, and economic perspectives. Hence, it is crucial to observe and regulate the city’s expansion to uphold sustainable planning and development. In this research, Landsat 5 and Landsat 8 for the years 1993, 2008, and 2021, as well as the integration of Shannon’s entropy model with GIS, were used to estimate changes in land cover and land use, as well as spatial dispersion and urban compactness. The results show that the built-up area increased dramatically from 137.7 km2 to 212.0 km2, which demonstrates 74.4 km2 of expansion, while the vegetation cover decreased significantly from 208.5 km2 to 173.7 km2, which shows a 34.8 km decline from 1993 to 2008. While between 2008 and 2021, the built-up area increased drastically from 212.0 km2 to 364.8 km2, which demonstrates 152.8 km2 of expansion, the vegetation cover decreased significantly from 173.7 km2 to 126.6 km2, which shows a 47.1 km2 decline. Furthermore, between 1993 and 2021, the built-up area expanded from 137.7 km2 to 364.8 km2, indicating a 227.2 km2 expansion, whereas the vegetation cover reduced significantly from 208.5 km2 to 126.6 km2, showing a −81.9 km2 reduction. Furthermore, the total values of relative Shannon’s entropy for the years 1993, 2008, and 2021 are 0.68, 0.71, and 0.70, respectively, which are closer to the upper limit of 1 and hence indicate the spatial dispersion within the study area during the study period. The findings of this research will serve as valuable tools for urban policymakers. They will aid in comprehending the spatial patterns of urban sprawl in Kabul city and in formulating appropriate strategies and policies. These measures aim to curtail the wasteful utilization of nonrenewable resources, maintain environmental equilibrium, address social inequalities, and promote comprehensive and sustainable development.

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“…This method is developed through the integration of ANN and fuzzy logic models which enables us to integrate learning capability and human knowledge together in one method and at the same time to cover many of their shortcomings such as lack of flexibility in ANN and finding out the correct positions and shapes for membership functions in FIS (Mohammady et al 2013). In this algorithm, during the learning process, membership functions in fuzzy structures change toward their optimal values (Mohammady et al 2013). In this paper, an ANFIS structure which generated a Sugeno-type Fuzzy Inference System (FIS) structure using subtractive clustering method has been used.…”

Section: Anfis Specificationmentioning

confidence: 99%

Optimization of adaptive neuro fuzzy inference system based urban growth model

Mohammady

2016

City Territ Archit

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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.

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Urban Growth Modeling Using Anfis Algorithm: A Case Study for Sanandaj City, Iran

Cited by 6 publications

References 28 publications

Urban Sprawl Monitoring

Urban Sprawl Monitoring

Evaluation of Urban Sprawl and Land Use/Cover Variation Patterns through Remote Sensing Data: A Case Study in Kabul City, Afghanistan

Optimization of adaptive neuro fuzzy inference system based urban growth model

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