Main content area

Should artificial neural networks replace linear models in tree ring based climate reconstructions?

Jevšenak, Jernej, Levanič, Tom
Dendrochronologia 2016 v.40 pp. 102-109
algorithms, climate, growth rings, linear models, neural networks, statistics, trees
Studies focused on tree ring—climate relationships usually use linear methods to find the optimal transfer function. In our study, three sites with three different tree species from the Western Balkan region were selected to compare linear and artificial neural network (ANN) nonlinear models and to see whether linear models can be potentially replaced with ANN in climate reconstruction. For each site, one linear and two different ANN models were calculated. For all analysed sites, we were able to find a better fit using the advanced technique of ANN. All calibration and verification statistics were in favour of ANN models. A climate variable was reconstructed for a selected site using linear and nonlinear ANN methods. We demonstrated that ANN is always a more effective method, which always produce better results than linear models. The key to success is a properly selected training algorithm, which prevents overfitting and is able to find the optimal transfer function, also linear, if that is the case.