Performance Comparison Artificial Neural Networks with Regression Analysis in Trees Trunk Volume Estimation

Document Type : Research Paper

Authors

1 M.Sc Student, Faculty of Natural Resources, University of Tarbiat Modares, I. R. Iran

2 Corresponding author, Assistant Prof, Faculty of Natural Resources, University of Tarbiat Modares, I. R. Iran

Abstract

Nowadays, regression analysis is a common method to estimate trees stem volume. Althought
trees trunk volume can be estimated with a certain accuracy, however there are many constraints
such as normality of variables and homogeneity of errors variance, when foresters use this
method. In this study, Artificial Neural Networks (ANN) as a subset of the technology of
Artificial Intelligence (AI), was used as a new method to estimate the volume trunk. For this
purpose, 101 trees were selected. Marked trees were located in Research and Educational
Forest of Tarbiat Modarres University (REFTMU). DBH, diameter at stump height, end
diameter trunk, trunk height and total tree height were mesuared with high accuracy during tree
marking. Two neural network models, multi-layer perceptron (MLP) and radial basis function
(RBF), were developed to estimate trunk volume. The results showed that with increasing the
number of variables, that have more correlation with trunk volume,correlation coefficient of
neural networks increased from 0.80 to .95. Also the RBF neural network was more accuracte
in trunk volume estimation than to MLP neural network. Comparing evaluation criteria for
ANN with stepwise regression showed that MLP and RBF neural networks had RMSE value
1.18 and 1.05, respectively while the RMSE value of regression was 2.57. Also the regression
correlation coefficient is less in compared with two models neural network.

Keywords

References

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