**5. Conclusion**

According to high valued importance of Golestan province forest study and researching and modeling of corruption amount of these forest seems to be urgent and important. In this research of Land Changer Modeler by using Markov chain utilizing three approach logistic regression, artificial neural network, and learning based on similarity weight sample, researching, and examination of forest cover changes has been done. Produced results from evaluating of model showed higher capability and power of land change modeler for predicting forest cover changes that after investigating and comparing dignity correctness of three modeling approaches based on three factors, ratio of Hits/False Alarms and figure of merit and ROC amount, results show a high level of efficiency and potential for artificial neural network and lower errors obtain from this method compared to other two approaches. In fact, the result shows artificial neural networks could correctly predict changes in pixels that have been changed in ground reality (Hit). On the other hand, models mistake pixels that have been changed in ground reality but stayed constant in prediction for artificial

neural network model compared to Logistic Regression Model and SIM weight model was lower (Miss). Also, mistakes and bugs resulted from the prediction of pixels that have stayed unchanged but changed in predicting model was also lower in the artificial neural network model compared to the other two approaches (false alarm).

This research's results have coordination with those of Mahiny and Turner who compared artificial neural networks to logistic regression. Also, Bayati et al. [46] compared two models of artificial neural networks and logistic regression in forest surveys. In their study, the artificial neural network model produced better results; In justifying this phenomenon, they stated that the reason for the difference between the better performance of artificial neural networks compared to statistical methods could be found in the ability to estimate and predict artificial neural networks with a small amount of data. This is despite the fact that the performance and accuracy of regression methods depend on the sample size strongly, and the small sample size can be a limitation of statistical models. Therefore, in the designed models of the artificial neural network, the small number of samples has not created a significant limitation.
