- - - Malayer University
- - - Behbahan Khatam Alanbia University of Technology
- - - Tarbiat Modares University
- - - Shahid Chamran University of Ahvaz

Purpose- Land is the place of earthly natural ecosystem functionality that has been used by humans in multiple methods. Land-use change (LUC) simulation is the most important method for researching LUC, which leads to some environmental issues such as the decreasing supply of forestry products and increasing levels of greenhouse gas emissions. Therefore, the present study aims at (i) using the Landsat imagery to prepare land use-cover (LULC) maps for ۲۰۰۰ and ۲۰۱۴; (ii) assessing Land use changes based on land change modeler (LCM) for the period from ۲۰۰۰ to ۲۰۱۴, and (iii) predicting the plausible land cover pattern in the region of Behbahan, using an algorithm based on ANN for ۲۰۲۸.Design/methodology/approach- A hybrid model consisting of a neural network model, Markov chain (MC), and cellular automata (CA Markov) was designed to improve the performance of the standard network model. The modeling of transfer power is done by multilayer Perceptron of an 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 were conducted in ۲۰۰۰ and ۲۰۱۴.Findings- Modeling results indicate that the model validation phase has a good ability to predict land-use change on the horizon is ۱۴ years old (۲۰۲۸). The comparison between modeling map and map related to ۲۰۱۳ shows that residential area and agricultural land continue to their growth trend so that residential area will be increased from ۳۱۵۷ hectares in ۲۰۱۴ to ۴۱۸۰ hectares in ۲۰۲۸ and it has ۲% growth that has been ۲% from ۲۰۰۰ to ۲۰۱۴. The results of this study can provide a suitable perspective for planners to manage land use regarding land-use changes in the 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.

Change detection, Neural Network, Markov chain, CA Markov, Behbehan County