**3.2 Predicting changes for year 2015 using artificial neural network model**

Demanding changes has occurred using Markov chain, and predicting changes for the year 2015 has been accomplished (**Figure 6**). Correction and analyze numbers of Skill Measure for examining term had been 84.52% and 0.6850 ordinarily that is declaring suitable and right prediction. The results of correction examining of this model show that the outcome resulted from model with ROC amount, 0.975 has high


**Table 2.**

*Values of the Cramer coefficient for driver variables.*

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

#### **Figure 6.**

*A: Actual map for 2015; B: Predicted map for 2015 using artificial neural network; C: Predicted map for 2015 using logistic regression; D: Predicted map for 2015 using SIM weight.*

harmony with occurred changes. The ratio of Hits/False Alarms for artificial multilayer perceptron neural network model is 63%, and the ratio of the figure of merit equals 12.

### **3.3 Predicting changes for year 2015 using logistic regression model**

In this case also demanding change has been occurred using Markov chain and changes by prediction for year 2015 has been done (**Figure 6**). Correction examining results logistic regression model shows that outcome resulted from model with ROC amount 0.922 has high harmony with occurred changes. Ratio Hits/False Alarms for logistic regression model is 50% and also amount of figure of merit is 10.

#### **3.4 Predicting changes for year 2015 using SIM weight**

In this case, demanding change has also been occurred by using Markov chain and has been done by prediction for year 2015 (**Figure 6**). Correction examining results for SIM Weight Process shows that ROC amount is 0.979. Ratio of Hits/False Alarms for SIM Weight is 52% based on similarity weight model, and also figure of merit is 10.

According to **Table 3**, artificial neural network, in two factors ratio Hits/False Alarms and figure of merit, for examining correction has shown higher quality and, compared to other processes, has more ability and capability for predicting forest


#### **Table 3.**

*Comparison between accuracy of the modeling of three approaches was used.*


#### **Table 4.**

*Areas under forest cover and nonforest during 1984, 2012, 2015, 2020, 2025, and 2030 (actual and predicted).*

coverage changes. The amount of ROC has not been observed so much differences between three approaches used. Therefore, artificial neural network model has been chosen as the best model for predicting forest changes prediction.

### **3.5 Predicting changes of land coverage by using artificial neural network method**

Results produced by researching forest cover changes in years 2015 to 2020 shows that in this time 11,561 acres of forest surface (equal to 46 % of region forest surface) been corrupted or ruined. Besides, around 2020 to 2025, approximately 11,147 forest surface (equal to 44 % of the region forest surface) will be declined. Examining forest cover changes between 2025 and 2030 also shows the reduction of 10,788 acres of region forest surface (equal to 42 %'s of region forest surface) (**Table 4**).
