Markov Chain

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 2000 and 2014; (ii) assessing Land use changes based on land change modeler (LCM) for the period from 2000 to 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- 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 2000 and 2014. Findings- 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 shows that residential area and agricultural land continue to their 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 has been 2% from 2000 to 2014. 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.