Modeling Tree-Ring Growth Responses to Climatic Variables Using Artificial Neural Networks
Abstract:Modeling the nonlinear and complex relationships between climate and tree-ring growth is of significance in dendroclimatic studies, but difficult to implement using traditional linear regression approaches. To overcome this difficulty, the technique of Artificial Neural Network (ANN) was employed in this study to develop the growth response models using the climate/growth database for Douglas-fir (Pseudotsuga menziesii var. menziesii [Mirb.] Franco) on southern Vancouver Island, Canada. The results show that the ANN models are able to extract nonlinear growth response patterns from the observed climate/tree-ring datasets, and to generate more accurate predictions than multiple linear regression approaches. The ANN-extracted climate-growth relationships can be displayed by scenario analysis; for example, when all other input variables are held fixed at their means, the limiting effect of April-July precipitation on tree growth decreases with increased precipitation. The main difficulty of applying ANN technique in dendroclimatology is the problem of overlearning (i.e., the ANN learns too many specific climate-growth patterns and loses the ability to generalize between similar climate-growth patterns). This problem can be alleviated by carefully designing the ANN, such as reducing the number of input variables, choosing a variety of training/testing sets, designing partially connected architectures with a small number of neurons in the hidden layer, and using early stopping during training process. The reliability of the derived ANN models is assessed by validation on independent testing datasets. The main advantages of the ANN technique over traditional dendroclimatic approaches are its ability to capture nonlinear climate-growth response, and its nonreliance on preassumed functional relationships for describing the observed datasets. The ANN method introduced in this article is sufficiently general to be applicable to many forest ecological modeling applications. For. Sci. 46(2):229-239.
Document Type: Journal Article
Publication date: 2000-05-01
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