A neural network approach to identify forest stands susceptible to wind damage

The artificial neural network technique to model wind damage to forests was examined. The network used in the investigation was a three-layered feed-forward neural network with a backpropagation training-algorithm using a momentum term and flat spot elimination. To yield insights into the performance of the network, a logistic regression model was fitted as a baseline. Two different types of models were set up and analyzed for both approaches. A dichotomous model that predicted the categories “damaged” versus “undamaged” for two different damage thresholds and a multinomial model that predicted the damage in four damage classes. The performance of the network and the logistic regression model was measured using the mean squared sensitivity error. The results of the dichotomous model demonstrate that a feed-forward network is able to better classify forests susceptible to wind damage than a logistic regression model, especially when the frequency of the undamaged and damaged forest stands differs significantly. This study also shows that the network has a higher capacity to identify damaged forest stands, compared to the logistic regression model applied in this investigation. With the specific dataset used in the present study, the proportion of damaged forest stands predicted by the network was between the observed proportion and the proportion predicted by the logistic regression model. The results of the multinomial models showed that both, the statistical model and the neural network were unable to classify all four damage classes but showed a dichotomous behavior in predicting the damage only in the two extreme damage classes. Possibilities to optimize the network performance by using different training algorithms or topologies and principal differences between the two models referring to their specific properties are discussed.