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

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
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.
Wydawca
Czasopismo
Rocznik
Strony
211--222
Opis fizyczny
Bibliogr. 60 poz., rys., tab.
Twórcy
autor
  • Slovenian Forestry Institute, Department of Forest Yield and Silviculture, Večna pot 2, 1000 Ljubljana, Slovenia
autor
  • Jožef Stefan Institute, Department of Knowledge Technologies, Jamova cesta 39, 1000 Ljubljana, Slovenia
  • Jožef Stefan International Postgraduate School, Jamova cesta 39, 1000 Ljubljana, Slovenia
autor
  • Slovenian Forestry Institute, Department of Forest Yield and Silviculture, Večna pot 2, 1000 Ljubljana, Slovenia
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Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-8b75fa9a-83ca-4ac1-b588-87faea7d2c8c
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