Reconstructing the air temperature from dendrochronological data from the Preolkhon area using the neural network method

Balybina, A. S.

doi:10.1016/j.gnr.2010.03.006

Cited by 7 publications

(3 citation statements)

References 3 publications

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“…Thus, given that our study occurred in a subtropical area, our focus turned to using machine learning methods. Artificial neural network (ANN) was considered as a better choice for careful assessment of complex climate-growth relationships [20,[36][37][38][39][40][41][42][43][44][45][46][47][48][49][50]. However, the learning method process of an ANN is a "black box operation" [51][52][53], meaning that it is sensitive to overfitting and ANNs have difficulty evaluating the contribution of each variable to the results, from a statistical point of view [53,54].…”

Section: Introductionmentioning

confidence: 99%

Insights into the BRT (Boosted Regression Trees) Method in the Study of the Climate-Growth Relationship of Masson Pine in Subtropical China

Wang

Shang

et al. 2019

Forests

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Dendroclimatology and dendroecology have entered mainstream dendrochronology research in subtropical and tropical areas. Our study focused on the use of the chronology series of Masson pine (Pinus massoniana Lamb.), the most widely distributed tree species in the subtropical wet monsoon climate regions in China, to understand the tree growth response to ecological and hydroclimatic variability. The boosted regression trees (BRT) model, a nonlinear machine learning method, was used to explore the complex relationship between tree-ring growth and climate factors on a larger spatial scale. The common pattern of an asymptotic growth response to the climate indicated that the climate-growth relationship may be linear until a certain threshold. Once beyond this threshold, tree growth will be insensitive to some climate factors, after which a nonlinear relationship may occur. Spring and autumn climate factors are important controls of tree growth in most study areas. General circulation model (GCM) projections of future climates suggest that warming climates, especially temperatures in excess of those of the optimum growth threshold (as estimated by BRT), will be particularly threatening to the adaptation of Masson pine.

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Section: Introductionmentioning

confidence: 99%

Insights into the BRT (Boosted Regression Trees) Method in the Study of the Climate-Growth Relationship of Masson Pine in Subtropical China

Wang

Shang

et al. 2019

Forests

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show abstract

“…To the best of our knowledge, our comparative study is the first in the dendroclimatological field to have used VLA tree-ring proxy and several climate predictors to compare different linear and nonlinear methods. In most previous comparative studies with tree-ring and climate data, ANN outperformed MLR (Balybina, 2010;D'Odorico et al, 2000;Jevšenak and Levanič, 2016;Zhang et al, 2000). We confirmed the high predictive abilities of ANN, which outperformed the other regression methods for both sites.…”

Section: Evaluation Of Various ML Algorithmsmentioning

confidence: 95%

“…There is growing evidence that nonlinear techniques are better at describing the relationship between tree-ring proxies and climate variables (Balybina, 2010;D'Odorico et al, 2000;Helama et al, 2009;Jevšenak and Levanič, 2016;Ni et al, 2002;Zhang et al, 2000). Sun et al (2017) showed a significant improvement over linear regression by using linear spline regression and a likelihood-based model to explain nonlinearity in the relationship between tree rings and precipitation.…”

Section: Introductionmentioning

confidence: 99%

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

2018

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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.

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Tree-ring-width based streamflow reconstruction based on the random forest algorithm for the source region of the Yangtze River, China

Wang

Zhang

2019

CATENA

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Reconstructing the air temperature from dendrochronological data from the Preolkhon area using the neural network method

Cited by 7 publications

References 3 publications

Insights into the BRT (Boosted Regression Trees) Method in the Study of the Climate-Growth Relationship of Masson Pine in Subtropical China

Insights into the BRT (Boosted Regression Trees) Method in the Study of the Climate-Growth Relationship of Masson Pine in Subtropical China

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

Tree-ring-width based streamflow reconstruction based on the random forest algorithm for the source region of the Yangtze River, China

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