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:: Volume 5, Issue 4 (3-2018) ::
jgit 2018, 5(4): 93-111 Back to browse issues page
Modeling and Prediction of Horizontal Urban Growth of Mashhad study region by Aggregating Cellular Automata, Fuzzy Theory, Neural Network and Logistic Regression
Farshad Rostami Galeh, Marjan Ghaemi, Rozbeh Shad *, Yasaman Lohrabi
University of Ferdowsi Mashhad
Abstract:   (2357 Views)
In this paper, we try to present a simple and powerful model to forecast the urban growth of Mashhad city applying a developed Cellular Automata (CA) algorithm in Geo-spatial information System (GIS). In spite of different CA's advantages in urban growth modeling, this model faces several limitations such as inability to model the uncertainties of urban systems and working based on experimental calibration (trial and error) techniques. In the proposed method, to overcome the uncertainty problem and increase the model efficiency, the fuzzy transition rules are introduced in the modeling process. Moreover, the effective criteria are weighted using the logistic regression algorithm to remove the second restriction and then the calibration process will be applied. Therefore, the prediction process of urban growth were implemented using a suggested simple and powerful model by aggregating different methods in a logical framework. For this purpose, Landsat 8 and ETM+ satellite images (between 2002-2015) were entered into the modeling process and the horizontal urban growth of Mashhad study area were predicted for 2028. The final obtained results showed that the proposed method with the Kappa coefficient of 54.8 and the overall accuracy of 92% is more accurate than conventional CA techniques.
Keywords: Fuzzy cellular automaton, anticipating, GIS, logistic regression, multilayer perception artificial neural network
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Type of Study: Research | Subject: GIS
Received: 2016/08/23 | Accepted: 2017/09/6 | Published: 2018/03/19
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Rostami Galeh F, Ghaemi M, Shad R, Lohrabi Y. Modeling and Prediction of Horizontal Urban Growth of Mashhad study region by Aggregating Cellular Automata, Fuzzy Theory, Neural Network and Logistic Regression. jgit. 2018; 5 (4) :93-111
URL: http://jgit.kntu.ac.ir/article-1-545-en.html


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Volume 5, Issue 4 (3-2018) Back to browse issues page
نشریه علمی-پژوهشی مهندسی فناوری اطلاعات مکانی Engineering Journal of Geospatial Information Technology
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