Estimating tree mortality of Norway spruce stands with neural networks
نویسندگان
چکیده
Within forest growth modeling LOGIT models are used to predict individual tree mortality. In this paper we present, Multi-Layer Perceptron, Learning Vector Quantization and Cascade Correlation networks as different formalisms for mortality predictions. The data set for parameterizing the LOGIT model and training the different neural network types comes from the Austrian National Forest Inventory. After training the different network types, we evaluate the resulting mortality predictions using an independent data set from the Litschau forest. The results indicate that Multi-Layer Perceptron with the learning algorithm resilient back-propagation and scaled conjugate gradient and Cascade Correlation with learning algorithm resilient back-propagation perform the best predictions. This suggests that neural networks are a viable alternative to the conventional LOGIT approach. 2001 Elsevier Science Ltd. All rights reserved.
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