AGRICULTURAL LAND CHANGE DETECTING AND FORECASTING USING COMBINATION OF FEEDFORWARD MULTILAYER NEURAL NETWORK, CELLULAR AUTOMATA AND MARKOV CHAIN MODELS
Keywords: Markov Chain, Cellular automata, Land use/cover change Prediction, Feedforward multilayer neural network
Abstract. This paper proposed a methodology for finding changes in agricultural land of Tehran during past years and simulating these changes for future years. The proposed method utilized the spatial GIS-based techniques and Landsat satellite imagery to predict agricultural land map for the future of Tehran. Therefore, a method for finding and predicting changes based on combining the feedforward multilayer perceptron neural network (MLP), cellular automata (CA), and Markov chain model were applied. In this regard, the Landsat images of 2002, 2008, and 2014 were classified by a binary support vector machine classifier into two classes of agricultural and non-agricultural. Then, the potential transition maps were generated by the neural network MLP and extensible areas were obtained by the Markov chain model. Finally, the results of these two steps were combined with the MOLA method and the 2020 and 2025 agricultural maps were predicted. The proposed method obtained the Kappa factor of 89.92% that indicates the high ability of the neural network and the CA–Markov for finding the changes and prediction in the city of Tehran.