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