<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Climate-growth relationships in <italic>Quercus robur</italic> chronologies for vessel lumen area (VLA) from two oak stands (QURO-1 and QURO-2) showed a consistent temperature signal: VLA is highly correlated with mean April temperature and the temperature at the end of the previous growing season. QURO-1 showed significant negative correlations with winter sums of precipitation. Selected climate variables were used as predictors of VLA in a comparison of various linear and nonlinear machine learning methods: Artificial Neural Networks (ANN), Multiple Linear Regression (MLR), Model Trees (MT), Bagging of Model Trees (BMT) and Random Forests of Regression Trees (RF). ANN outperformed all the other regression algorithms at both sites. Good performance also characterised RF and BMT, while MLR, and especially MT, displayed weaker performance. Based on our results, advanced machine learning algorithms should be seriously considered in future climate reconstructions.</p></abstract>

Climate-growth relationships in Quercus robur chronologies for vessel lumen area (VLA) from two oak stands (QURO-1 and QURO-2) showed a consistent temperature signal: VLA is highly correlated with mean April temperature and the temperature at the end of the previous growing season. QURO-1 showed significant negative correlations with winter sums of precipitation. Selected climate variables were used as predictors of VLA in a comparison of various linear and nonlinear machine learning methods: Artificial Neural Networks (ANN), Multiple Linear Regression (MLR), Model Trees (MT), Bagging of Model Trees (BMT) and Random Forests of Regression Trees (RF). ANN outperformed all the other regression algorithms at both sites. Good performance also characterised RF and BMT, while MLR, and especially MT, displayed weaker performance. Based on our results, advanced machine learning algorithms should be seriously considered in future climate reconstructions.

Predicting the vessel lumen area tree-ring parameter of <italic>Quercus robur</italic> with linear and nonlinear machine learning algorithms

Department of Forest Yield and Silviculture

Geochronometria

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Predicting the vessel lumen area tree-ring parameter of  with linear and nonlinear machine learning algorithms

Climate-growth relationships in Quercus robur chronologies for vessel lumen area (VLA) from two oak stands (QURO-1 and QURO-2) showed a consistent temperature...

Predicting the vessel lumen area tree-ring parameter of Quercus robur with linear and nonlinear machine learning algorithms

Article Category: Regular Articles

Pubblicato online: 05 nov 2018

Pagine: 211 - 222

Ricevuto: 26 gen 2018

Accettato: 28 ago 2018

DOI: https://doi.org/10.1515/geochr-2015-0097

Parole chiave
dendroclimatology, artificial neural networks, multiple linear regression, machine learning, vessel lumen area,

© 2018 J. Jevšenak.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Predicting the vessel lumen area tree-ring parameter of Quercus robur with linear and nonlinear machine learning algorithms

Article Category: Regular Articles

Pubblicato online: 05 nov 2018

Pagine: 211 - 222

Ricevuto: 26 gen 2018

Accettato: 28 ago 2018

DOI: https://doi.org/10.1515/geochr-2015-0097

Parole chiavedendroclimatology, artificial neural networks, multiple linear regression, machine learning, vessel lumen area,

© 2018 J. Jevšenak.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Parole chiave
dendroclimatology, artificial neural networks, multiple linear regression, machine learning, vessel lumen area,