A Machine Learning Approach to Analyzing the Relationship Between Temperatures and Multi-Proxy Tree-Ring Records

Abstract

Abstract Machine learning (ML) is a widely unexplored field in dendroclimatology, but it is a powerful tool that might improve the accuracy of climate reconstructions. In this paper, different ML algorithms are compared to climate reconstruction from tree-ring proxies. The algorithms considered are multiple linear regression (MLR), artificial neural networks (ANN), model trees (MT), bagging of model trees (BMT), and random forests of regression trees (RF). April-May mean temperature at a Quercus robur stand in Slovenia is predicted with mean vessel area (MVA, correlation coefficient with April-May mean temperature, r = 0.70, p 0.05 (ns)) chronologies. The predictive performance of ML algorithms was estimated by 3-fold cross-validation repeated 100 times. I...