**1. Introduction**

The change in forest cover plays an essential role in ecosystem services, the carbon balance in the atmosphere, and climate change [1, 2]. The forest ecosystems of Gorganrood watershed play a very important role in controlling surface runoff and

reducing floods, protecting surface soil and reducing soil erosion, adjusting temperature, and reducing the amount of greenhouse gases.

In consideration of the process of forest destruction in recent decades, it is valuable to examine the changes that have occurred in the fields of natural resources and to interpret the cause and extent of these changes and their impact on other sources [3]. Golestan province forests are very important among the forests in the north of Iran, because of their climatic conditions [4]. Golestan province forests have the highest annual destruction rate among the other northern provinces [5].

So far, many studies have used remote sensing (RS) and geographic information system (GIS) methods to effectively monitor forest cover changes [6–10]. Mahiny and Turner [11] modeled vegetation changes in the watershed of the Boorowa river in Australia by using artificial neural network and logistic regression. The results of their study indicated that artificial neural networks generally achieved better outcomes than logistic regression. Mas et al. [12] in a study that modeled deforestation by GIS and artificial neural network, and the results of their study showed that the rate of forest destruction was higher in areas with gentle slopes, high altitudes, and fertile soils. In addition, their outcomes demonstrated that the intensity of deforestation is greatly reduced by maintaining distance from the road and residential areas. Khoi and Murayama [13] modeled deforestation in an area in northern Vietnam using artificial neural networks and Markov chain models. They found that the destruction is intensive in the borders between forests and agricultural lands, fields near water sources, and areas with lower altitudes. Kumar et al. [14] modeled and predicted forest cover changes in forested areas in India. In order to explain the effects of human interventions in the forest, they used three distance variables, as explanatory variables for forest change (the distance from the edge of the forest, the road, and the city, and the map of the slope classes). The highest regression coefficient (β = 26.892) was related to the distance from the forest, which present that changes in the forest are more significant near the edge of the forest. Bagheri and Shetaei [15] in the research titled modeling the reduction of forest extent using logistic regression in the Chehel Chai watershed of Golestan province during the years 2016 to 2015, determined that the variables of the slope, distance from the village and the road have an inverse relationship with the amount of destruction. In addition, with the increase in height above the sea level in this area, the amount of destruction increased. Hassanzadeh [16] modeled deforestation in Falred forests by using the multivariate methods and artificial neural networks. According to the estimation error of modeling, artificial neural networks are a better method for modeling such variables than multivariate regression. In a study conducted by Arkhi et al. [17], the forests of North Ilam were simulated using logistic regression. According to the modeling results, it was found that the slope variables, the distance from the population centers and the road, have an opposite relationship with the amount of destruction, and the increase of the height above the sea level in this area resulted in decreasing the amount of destruction. Sardarzadeh et al. [18] predicted the destruction of forests in Chehel Chai watershed of Golestan province using artificial neural networks and Markov chain analysis. The obtained results indicate the destruction of 15.8% of dense forests during the years 1988 to 2010. Gholamalifard et al. [19] conducted a study with the aim of comparing logistic regression and artificial neural networks for modeling the transfer potential of coastal land cover change in Mazandaran province. The results showed that logistic regression has higher accuracy.

*A Study of the Comparison between Artificial Neural Networks, Logistic Regression and Similarity… DOI: http://dx.doi.org/10.5772/intechopen.111615*
