Urban Growth Modeling Considering Educational Institutions In Qazvin, Iran: An Investigation Into The Performance of Three ANFIS Methods

2021 ◽  
Author(s):  
Yousef Ghobadiha ◽  
Hamid Motieyan
Keyword(s):  
Cellular Automata ◽  
Urban Growth ◽  
Urban Expansion ◽  
Growth Modeling ◽  
Rapid Expansion ◽  
Subtractive Clustering ◽  
Time Interval ◽  
Inference System ◽  
Automatic Production ◽  
Urban Growth Modeling

Abstract Due to increasing urbanization, the rapid expansion of urban spaces has become a major environmental concern over the last few decades. Therefore, modeling the urban expansion as a complex system has been scrutinized in recent years; however, determining the rules that lead to the expansion of urban areas has always been a challenging factor in this field, especially for disaggregated models like cellular automata (CA). To overcome this issue, in this research, an Adaptive Network-based Fuzzy Inference System (ANFIS) is proposed to enhance the simulation of urban growth through the automatic production of transition rules. The ANFIS can be associated with several inputs division methods, such as ANFIS accompanied by grid partitioning (ANFIS-GP), subtractive clustering (ANFIS-SC), and fuzzy c-means clustering (ANFIS-FCM). Hence, twenty-two ANFIS models based on Landsat images for the time interval from 2000 to 2010 and using different division methods were trained to investigate their effect on the efficiency of ANFIS in urban growth modeling. To examine the efficiency, the Cellular Automata-based Markov Chain (CA-MC) as a popular method was developed, and the simulation accuracy of CA-MC and the most accurate ANFIS models were obtained through comparison with observed data. The most accurate ANFIS-SC model had a Kappa of 0.76 and an overall accuracy of 93.41% for the 2019 simulated map. The results from this study reveal that the ANFIS model is effective at simulating urban expansion and the ANFIS-SC is superior to CA-MC, ANFIS-GP, and ANFIS-FCM models in urban expansion modeling.

scholarly journals Urban Growth Modeling Considering Educational Institutions: An Investigation Into the Performance of Three Anfis Methods

2021 ◽  
Author(s):  
Yousef Ghobadiha ◽  
Hamid Motieyan
Keyword(s):  
Cellular Automata ◽  
Urban Growth ◽  
Urban Expansion ◽  
Growth Modeling ◽  
Rapid Expansion ◽  
Subtractive Clustering ◽  
Time Interval ◽  
Inference System ◽  
Automatic Production ◽  
Urban Growth Modeling

Abstract Due to increasing urbanization, the rapid expansion of urban spaces has become a major environmental concern over the last few decades. Therefore, modeling the urban expansion as a complex system has been scrutinized in recent years; however, determining the rules that lead to the expansion of urban areas has always been a challenging factor in this field, especially for disaggregated models like cellular automata (CA). To overcome this issue, in this research, an Adaptive Network-based Fuzzy Inference System (ANFIS) is proposed to enhance the simulation of urban growth through the automatic production of transition rules. The ANFIS can be associated with several inputs division methods, such as ANFIS accompanied by grid partitioning (ANFIS-GP), subtractive clustering (ANFIS-SC), and fuzzy c-means clustering (ANFIS-FCM). Hence, twenty-two ANFIS models based on Landsat images for the time interval from 2000 to 2010 and using different division methods were trained to investigate their effect on the efficiency of ANFIS in urban growth modeling. To examine the efficiency, the Cellular Automata-based Markov Chain (CA-MC) as a popular method was developed, and the simulation accuracy of CA-MC and the most accurate ANFIS models were obtained through comparison with observed data. The most accurate ANFIS-SC model had a Kappa of 0.76 and an overall accuracy of 93.41% for the 2019 simulated map. The results from this study reveal that the ANFIS model is effective at simulating urban expansion and the ANFIS-SC is superior to CA-MC, ANFIS-GP, and ANFIS-FCM models in urban expansion modeling.

scholarly journals Modeling of Urban Growth Using Cellular Automata and GIS Case of Benslimane in Morocco

2020 ◽  
Author(s):  
BENCHELHA MOHAMED ◽  
Benzha Fatiha ◽  
Rhinane Hassan ◽  
BENCHELHA SAID ◽  
BENCHELHA TAOUFIK ◽  
...  
Keyword(s):  
Land Use ◽  
Cellular Automata ◽  
Urban Growth ◽  
Urban Expansion ◽  
Confusion Matrix ◽  
Spatial Transformation ◽  
Urban Growth Modeling ◽  
Main Component Analysis ◽  
Spatio Temporal ◽  
Main Component

In this study, our goal was to research land-use change by combining spatio–temporal land use/land cover monitoring (LULC (1989–2019) and urban growth modeling (1999–2039) in Benslimane, Morocco, to determine the effect of urban growth on different groups based on cellular automata (CA) and geospatial methods. A further goal was to test the reliability of the AC algorithm for urban expansion modeling. To do this, four years of satellite data were used at the same time as population density, downtown distance, slope, and ground road distance. The LULC satellite reported a rise of 3.8 km2 (318% variation) during 1989–2019. Spatial transformation analysis reveals a good classification similarity ranging from 89% to 91% with the main component analysis (PCA) technique. The statistical accuracy between the satellite scale and the replicated built region of 2019 gave 97.23 %t of the confusion matrix overall accuracy, and the region under the receiver operational characteristics (ROC) curve to 0.94, suggesting the model's high accuracy. Although the constructed area remains low relative to the total area of the municipality's territory, the LULC project shows that the urban area will extend to 5,044 km2 in 2019, principally in the western and southwestern sections. In 2019–2039, urban development is expected to lead to a transformation of the other class (loss of 1,364 km2), followed by vegetation cover (loss of 0.345 km2). In spatial modeling and statistical calculations, the GDAL and NumPy Python 3.8 libraries were successful.

scholarly journals Urban growth modeling using cellular automata model and AHP (case study: Qazvin city)

2019 ◽  
Vol 6 (1) ◽  
pp. 235-248 ◽  
Author(s):  
Nahid Falah ◽  
Alireza Karimi ◽  
Ali Tavakoli Harandi
Keyword(s):  
Cellular Automata ◽  
Urban Growth ◽  
Quantitative Methods ◽  
Growth Modeling ◽  
Kappa Coefficient ◽  
Data Availability ◽  
Main Role ◽  
Cellular Automata Model ◽  
Urban Growth Modeling ◽  
The City

AbstractIrregular growth in the surrounding lands is one of the most important issues for the city managers and programmers at various levels. Whereas nowadays study the process of land use changes to urban use plays the main role in long time decisions and programs, predicting the process of city growth and its modeling in future with precise methods for management and urban expansion control will be necessary more than other times. One of urban growth modeling is cellular automata model. This model has been used widely in urban studies because of its dynamic nature, ability of Integration with other models, ability to modify the model and required data availability. In this article, to maximize the efficiency of the cellular automata model and its constraints, the integration of the AHP automated cell model and cellular automata model have been used; and its accuracy has been evaluated. This article has been practical because its related principles has been collected in a documentary manner and has been used to analyses the issue in comparative and quantitative methods. Initially, the unplanned growth of Qazvin city has been investigated by Holdern and Shannon model. Then main parameters including distance from roads, land prices, distance from faults, distance from the rivers, soil gender, slope, permission to build land, topography, landscape, view to gardens and forest park as parameters involved in the development of Qazvin city are considered. The input data used in this research are Landsat tm and DEM images of the city of Qazvin in 1996 and 2016. Also, to evaluate the correctness of the model responses, the map of the developed regions in 2016 and the Kappa coefficient have been used. The Kappa coefficient is 92.3%, which is considered significant and appropriate and gave the fact that the Kappa number is acceptable. The Qazvin simulation was made in 2026. The results show that the proposed integrated model is suitable for studying urban growth.

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