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The classical methods, also known as hard methods, are based on the accuracy of calculations, while the real world is founded on the inaccuracy of boundaries and the uncertainties, which is more consistent with soft computing methods. Each of these methods has its own strengths and weaknesses, and the hybridization theory was introduced to solve these problems. In the hybridization theory, which is also called intelligent hybrid systems, two or more single intelligent methods are combined to eliminate or rectify the shortcomings and limitations of single methods. In this study, forest degradation was modeled by employing the single-perceptron neural network and hybrid neuro-fuzzy method. For this purpose, the images from Landsat-5 TM sensor in 1999 and Landsat 8 OLI sensor in 2017 were utilized. Then, the degraded and non-degraded forest areas were sampled in 200 locations. Seven factors identified as the most effective factors in forest degradation, including the distance from the features like city, river, village, sea, and road, elevation and slope were measured for the 200 locations. The mean squared error (MSE) was used to evaluate the performance of models, which was 0.0535, 0.0704, and 0.0908 for the perceptron neural network in the Levenberg-Marquardt, Bayesian regularization, and scaled conjugate gradient algorithms, respectively. Also, the MSE value for the neuro-fuzzy model in the optimization and hybrid algorithms was 0.0190 and 0.0102, respectively. The analysis of the results showed the optimal performance of the neuro-fuzzy method both in reducing the error and in generalizing the model. Relying on the uncertainty rule, the neuro-fuzzy model provides the conditions that are closer to reality and have been more successful than the perceptron model at selecting the appropriate data.

Keywords: Intelligent Modeling, Multilayer Perceptron, Neural-Fuzzy, Forest Degradation

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