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13.
A machine learning approach to analyzing the relationship between temperatures and multi-proxy tree-ring records
Jevšenak, Jernej ...
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 < 0.001) and earlywood width (EW, r = %0.28, p < 0.05). Similarly, June-August mean temperature is predicted with stable carbon isotope (%13C, r = 0.72, p < 0.001), stable oxygen isotope (%18O, r = 0.32, p < 0.05) and tree-ring width (TRW, r = 0.11, p > 0.05 (ns)) chronologies. The predictive performance of ML algorithms was estimated by 3-fold cross-validation repeated 100 times. In both spring and summer temperature models, BMT performed best respectively in 62% and 52% of the 100 repetitions. The second-best method was ANN. Although BMT gave the best validation results, the differences in the models% performances were minor. We therefore recommend always comparing different ML regression techniques and selecting the optimal one for applications in dendroclimatology.
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