DIGITALNA ARHIVA ŠUMARSKOG LISTA
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ŠUMARSKI LIST 3-4/2009 str. 41 <-- 41 --> PDF |
D. Klobučar, R. Pernar: UMJETNE NEURONSKE MREŽE U PROCJENI SASTOJINSKIH OBRASTA...Šumarski list br. 3–4, CXXXIII (2009), 145-155 There are numerous variations of error back propagation algorithms. As for the early stopping method, it is not advisable to use an algorithm which converges too rapidly (Xiangcheng et al. 2005, Demuth et al. 2006). Consequently, two algorithms were used: resilient back-propagation and scaled conjugate gradient algorithm. Prior to training the neural network itself, the data were preprocessed. In this sense, two operations were performed using MATLAB functions: normalization of input-output values and analysis of the main components of input values. Training encompassed a total of seven algorithm models with error back propagation with one or two hidden layers containing a different number of hidden neurons. Different activation functions were also applied in hidden and output layers. Self-organizing neural network was used to control densities according to their distribution into three categories (normal, less than normal, poor). To study the applicability of this neural network, 80 compartments/subcompartments were divided into two sets: training set and testing set, each consisting of 40 compartments/subcompartments. The data were preprocessed before the neural network was trained, just as was the case with the multilayer perceptron. Textural features of first order histograms (arithmetic means, standard deviation, smoothness, third moment, evenness and entropy) and second order histograms (absolute value of difference, inertia, covariance, entropy and energy) were used as input data for the neural network, whereas output density values were taken from the Management plan. Output values may also be represented as the number of trees, basal area or volume per hectare or as some other quantitative and qualitative stand values. Stand density was used as an output value for two reasons: a) poorer spectral features of the applied photographs, and b) the fact that, from the aspect of the forestry profession, the photographs were obtained in the unfavorable period (time of the year in which the ground is the least covered with vegetation). To test the difference in stand density values between the data from the Management plan and the optimal model of artificial neural network, the analysis of variance for repeated measurements was used. Research confirmed good generalization characteristics of a multilayer perceptron in density estimation, as well as the fact that a self-organizing neural network can be used to control and distribute stand densities. The applied procedure of density estimation achieves an acceptable accuracy and a high degree of automatism, which removes the subjective nature of classical remote sensing methods. This research confirmed the advantages and disadvantages of artificial neural networks. The advantages are as follows: it is not necessary to know data models, the networks can be used to analyze new conditions, and they tolerate imperfect data. The disadvantages are: the need to determine optimal architecture and the impossibility of estimation outside the scope of learning data values. However, despite their numerous advantages, artificial neural networks will not completely replace classical statistical methods. Instead, a dual approach and integration of these two techniques in decision making processes will be a very useful tool in forest resource management of the 21st century. They are currently broadly applied, so we could say that this is a time of transition to the technology of artificial neural networks. Consequently, forestry of the Republic of Croatia should make broader use of this new technology. Key words:artificial neural networks, remote sensing, cyclic aerial photographs, density, texture |