An Artificial Neural Networks‐Based Tree Ring Width Proxy System Model for Paleoclimate Data Assimilation
An Artificial Neural Networks‐Based Tree Ring Width Proxy System Model for Paleoclimate Data Assimilation
复制标题
基于人工神经网络的古气候数据同化树木年轮宽度代理系统模型
DOI：
10.1029/2018ms001525
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发表时间：
2019-04
影响因子：
6.8
通讯作者：
Xin Li
中科院分区：
文献类型：
--
作者：
Miao Fang;Xin Li
构建合适的树木年轮宽度（TRW）替代系统模型（PSMs）是古气候数据同化（PDA）中一个新兴的研究焦点。然而，目前对于哪些树木年轮宽度替代系统模型对于实际的古气候数据同化应用是最优的还不清楚。本研究……
Constructing suitable tree ring width (TRW) proxy system models (PSMs) is an emerging research focus in paleoclimate data assimilation (PDA). Currently, however, it is unknown as to which TRW PSMs are optimal for practical PDA applications. This study proposes an artificial neural networks (ANN)‐based TRW PSM and compares its performance with those of existing TRW PSMs, including linear univariate model, linear multivariate model, and physically based VS‐Lite model. The results show that ANN‐based TRW PSM is more suitable for practical PDA applications than other three TRW PSMs in terms of performance and universality. Overall, the performances of the four TRW PSMs in PDA can be ranked as follows (from best to worst): ANN, linear multivariate model, linear univariate model, and physically based VS‐Lite model. In addition, the results of our study not only indicate that the ANN model is a really effective tool for constructing TRW PSM in practical PDA applications but also imply that the ANN model has the potential to provide new insights into the construction of other types of PSMs (e.g., speleothem δ18O PSM) when physics of the climate‐proxy relationships cannot be described fully in advance.
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DOI：
10.1191/0959683605hl819rp
发表时间：
2005-05
期刊：
The Holocene
影响因子：
--
作者：
J. Guiot;A. Nicault;C. Rathgeber;Jl Edouard;F. Guibal;G. Pichard;C. Till
通讯作者：
J. Guiot;A. Nicault;C. Rathgeber;Jl Edouard;F. Guibal;G. Pichard;C. Till
DOI：
10.1201/9781315121062-5
发表时间：
2019-02
期刊：
Statistical Methods for Materials Science
影响因子：
--
作者：
M. De Graef
通讯作者：
M. De Graef
影响因子：
1.4
作者：
Qi-Bin Zhang;R. Hebda;Qi-Jun Zhang;René I. Alfaro
通讯作者：
Qi-Bin Zhang;R. Hebda;Qi-Jun Zhang;René I. Alfaro
影响因子：
4.9
作者：
N. Steiger;G. Hakim;E. Steig;D. Battisti;G. Roe
通讯作者：
N. Steiger;G. Hakim;E. Steig;D. Battisti;G. Roe
DOI：
10.1007/b98890
发表时间：
1998
期刊：
--
影响因子：
--
作者：
J. O. Rawlings;S. Pantula;David A. Dickey
通讯作者：
J. O. Rawlings;S. Pantula;David A. Dickey