Integration of Neural Network, Markov Chain and CA Markov Models to Simulate Land use Change Region of Behbahan County

Abstract

Purpose- Important land use typical of human impact on the environment and in the past half century, land use changes has been the largest and most complete. the modeling of land cover changes provides valuable information for understanding changes process, determining effective factors and forecasting areas which are exposed to changes. Therefore, the present study aims at (i) using the Landsat imagery to prepare land use- cover (LULC) maps for 2000 and 2014; (ii) assessing Land use changes on the basis of land change modeler (LCM) for the period from 2000 and 2014, and (iii) predicting the plausible land cover pattern in the region of Behbahan using an algorithm based on ANN for 2028. Design/methodology/approach- Hybrid model consisting of neural network model, Markov chain (MC), and cellular automata (CA Markov) were designed to improve the performance of the standard network model. The modeling of transfer power is done by multilayer Perceptron of artificial neural network and six variables. The change allocated to each use and the forecasting is computed by Markov chain and CA Markov. Operation model calibration and verification of land use data at two points, was conducted in 2000 and 2014 Finding- Modeling results indicate that the model validation phase, has a good ability to predict land use change on the horizon is 14 years old (2028). The comparison between modeling map and map related to 2013 show that residential area and agricultural land continue to their own growth trend so that residential area will be increased from 3157 hectares in 2014 to 4180 hectares in 2028 and it has 2% growth that this level of growth has been 2% from 2000 to 2014. Results of this study can provide suitable perspective for planners to manage land use with regard to land use changes in past, present and future. They are also can be used for development assessment projects, the cumulative effects assessment, and the vulnerable and sensitive zone recognition