Artificial neural networks and dendroclimatic reconstructions: an example from the Front Range, Colorado, USA

Abstract

The feasibility of reconstructing total spring precipitation for the South Platte River basin from tree-ring chronologies using artificial neural networks is explored. The use of artificial neural networks allows a comparison of reconstructions resulting from both linear and nonlinear models. Both types of models produced reconstructions that explained more than 40% of the variation in spring precipitation and were well verified with independent data. Although the nonlinear models produced higher R2 values than did the linear model for the calibration period, they performed less well in the independent period. This result and other model evaluation statistics suggest that, in this study, the nonlinear models contain a greater degree of overfit than the linear model, and thus, do not offer a clear improvement over the linear model for the reconstruction of spring precipitation in this region. However, neural networks offer an alternative approach to linear regression techniques and may provide improved dendroclimatic reconstructions in other areas.