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.

中文翻译：

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用线性和非线性机器学习算法预测栎属植物血管腔面积树轮参数

摘要 来自两个橡树林（QURO-1 和 QURO-2）的血管腔面积 (VLA) 的 Quercus robur 年表中的气候生长关系显示出一致的温度信号：VLA 与 4 月平均温度和 4 月末的温度高度相关。上一个生长季节。QURO-1 与冬季降水总量呈显着负相关。在比较各种线性和非线性机器学习方法时，选定的气候变量被用作 VLA 的预测因子：人工神经网络 (ANN)、多元线性回归 (MLR)、模型树 (MT)、模型树装袋 (BMT) 和随机回归树森林 (RF)。ANN 在两个站点都优于所有其他回归算法。RF 和 BMT 也表现出良好的性能，而 MLR，尤其是 MT，表现出较弱的性能。