A multiple land use change model based on artificial neural network, Markov chain, and multi objective land allocation
نویسندگان
1 School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
2 School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
3 Department of Civil Engineering, Shahrood University of Technology, Shahrood, Iran
doi
10.22059/eoge.2017.220342.1006چکیده
In this paper, a new combination of Artificial Neural Network (ANN), Markov Chain (MC), and MultiObjective Land Allocation (MOLA) was proposed and evaluated to simulate multiple land use changesusing GIS-based techniques and multi temporal remote sensing data. The main objective of this paper is topredict land use changes for Tehran, the biggest and capital city of Iran. In this regard, by integration ofANN, MC, and MOLA, we found the pixels that have the highest tendency to change their states from oneland use category to others. An ANN model was applied to create Transition Potential Maps (TPMs), andan MC model was used to calculate the quantity of the changes. Finally, a MOLA model was employed forspatial allocation of new changes. In order to analyze the effects of proximity, three types of neighborhoodfilters were combined with MOLA. The proposed method achieved 92.62%, 95.49%, and 92.74% of kappaindex of agreement (KIA), overall accuracy (OA), and kappa of location (Klocation), respectively. Thismethod was applied for Tehran to predict the situation in year 2020. The trend of the changes shows thatthe urban growth is moving toward southwest of the city, where the areas with poor infrastructure aresituated.
