Pahlavani, Parham, Askarian Omran, Hosein, Bigdeli, Behnaz. (1396). A multiple land use change model based on artificial neural network, Markov chain, and multi objective land allocation. , 1(2), 82-99. doi: 10.22059/eoge.2017.220342.1006
Parham Pahlavani; Hosein Askarian Omran; Behnaz Bigdeli. "A multiple land use change model based on artificial neural network, Markov chain, and multi objective land allocation". , 1, 2, 1396, 82-99. doi: 10.22059/eoge.2017.220342.1006
Pahlavani, Parham, Askarian Omran, Hosein, Bigdeli, Behnaz. (1396). 'A multiple land use change model based on artificial neural network, Markov chain, and multi objective land allocation', , 1(2), pp. 82-99. doi: 10.22059/eoge.2017.220342.1006
Pahlavani, Parham, Askarian Omran, Hosein, Bigdeli, Behnaz. A multiple land use change model based on artificial neural network, Markov chain, and multi objective land allocation. , 1396; 1(2): 82-99. doi: 10.22059/eoge.2017.220342.1006

A multiple land use change model based on artificial neural network, Markov chain, and multi objective land allocation

Earth Observation and Geomatics Engineering
مقاله 2، دوره 1، شماره 2، 2017، صفحه 82-99    اصل مقاله (2.86 M)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22059/eoge.2017.220342.1006
نویسندگان
Parham Pahlavani* 1؛ Hosein Askarian Omran1؛ Behnaz Bigdeli2
1School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
2Department of Civil Engineering, Shahrood University of Technology, Shahrood, Iran
چکیده
In this paper, a new combination of Artificial Neural Network (ANN), Markov Chain (MC), and Multi
Objective Land Allocation (MOLA) was proposed and evaluated to simulate multiple land use changes
using GIS-based techniques and multi temporal remote sensing data. The main objective of this paper is to
predict land use changes for Tehran, the biggest and capital city of Iran. In this regard, by integration of
ANN, MC, and MOLA, we found the pixels that have the highest tendency to change their states from one
land use category to others. An ANN model was applied to create Transition Potential Maps (TPMs), and
an MC model was used to calculate the quantity of the changes. Finally, a MOLA model was employed for
spatial allocation of new changes. In order to analyze the effects of proximity, three types of neighborhood
filters were combined with MOLA. The proposed method achieved 92.62%, 95.49%, and 92.74% of kappa
index of agreement (KIA), overall accuracy (OA), and kappa of location (Klocation), respectively. This
method was applied for Tehran to predict the situation in year 2020. The trend of the changes shows that
the urban growth is moving toward southwest of the city, where the areas with poor infrastructure are
situated.
کلیدواژه‌ها
Multiple land use changes؛ Artificial Neural Network؛ Markov Chain؛ Multi objective land allocation؛ Neighborhood filter
مراجع
آمار
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