**4. Discussion**

Although many methods are available to model land transition potentials, they are usually not user-friendly and require the specification of many parameters, making the task difficult for decision-makers not familiar with the tools, as well as making the process difficult to interpret. SimWeight is an instance-based learning algorithm based on the logic of the K-nearest neighbor algorithm. The method identifies the relevance of each driver variable and predicts the transition potential of locations given known instances of change. Although computationally simple, the method cannot handle complex nonlinear relations, which is often visible in built-up growth in heterogeneous environments. However, this method can be useful for areas to project infilling or edge-expansion type of built-up growth [43]. SimWeight focused on the distance from the past transitions producing most potential zones near the

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

change areas, LogReg produced suitable areas considering the linear relationship between driving factors and the built-up change.

While the LogReg approach is straightforward and easy to reproduce, it cannot mimic the complex relationship between the variables and the land use pattern in the changing space. This, therefore, indicates the unsuitability of the method for prediction, especially for dynamic and heterogeneous built-up growth. However, this statement might be applicable for only short-time-scale studies and might not be true for applications that incorporate larger time-scale predictions [44].

The Artificial neural networks, as it iterates multiple times to produce the best fit between the transition and driving variables are able to estimate high change potentials for areas of actual change. Artificial neural networks are a sum of the complex of improving methods that can analyze and calculate nonlinear relations after well training and adjusting weights between income and outcome parameters by high currency. Although the SIM weight model and also logistic regression model are not able to calculate nonlinear relations between variants [40]. Artificial neural networks compared to Logistic Regression Model and SIM weight model, do not need a specific formulation for the statement relation between income and outcome data; otherwise, the relation between income and outcome data has been taken by the learning process [45]. At last, using the artificial neural network method to predict jungle cover changes in the future (years 2020, 2025, and 2030) has been discussed.

In relation to the better performance of the artificial neural network, we can mention the following: high processing speed, the ability to learn the pattern, the ability to generalize the pattern after learning, flexibility against unwanted errors, and not causing significant disruption in case of problems in part of the connections due to the distribution of network weights. This discussion declares that artificial neural network has higher power capability for predicting forest changes.

The results of this part demonstrate forest cover changes in the Gorganroud watershed as well and declare that continuing of the current process in recent 30 years, what kind of problems and huge big enormous obtains, and obstacles to the forest of this region will occur. Putting an obvious clear picture of the future in front of Managers and program makers, scheduling Personals can be efficient and effective capable in scheduling for saving and cohabitating forest regions.
