Modeling Tree-Ring Growth Responses to Climatic Variables Using Artificial Neural Networks
Modeling Tree-Ring Growth Responses to Climatic Variables Using Artificial Neural Networks
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DOI：
10.1093/forestscience/46.2.229
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发表时间：
2000-05
期刊：
影响因子：
1.4
通讯作者：
Qi-Bin Zhang;R. Hebda;Qi-Jun Zhang;René I. Alfaro
中科院分区：
文献类型：
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作者：
Qi-Bin Zhang;R. Hebda;Qi-Jun Zhang;René I. Alfaro
在树木年轮气候学研究中，对气候与树木年轮生长之间的非线性复杂关系进行建模具有重要意义，但使用传统的线性回归方法难以实现。为克服这一困难，本研究采用人工神经网络（ANN）技术，利用加拿大温哥华岛南部花旗松（Pseudotsuga menziesii var. menziesii [Mirb.] Franco）的气候/生长数据库来建立生长响应模型。结果表明，ANN模型能够从观测到的气候/树木年轮数据集中提取非线性生长响应模式，并且比多元线性回归方法能做出更准确的预测。通过情景分析可以展示ANN提取的气候 - 生长关系；例如，当所有其他输入变量均固定在其均值时，4 - 7月降水量对树木生长的限制作用随着降水量的增加而减小。在树木年轮气候学中应用ANN技术的主要困难是过学习问题（即ANN学习了过多特定的气候 - 生长模式，失去了在相似气候 - 生长模式之间进行归纳的能力）。通过精心设计ANN可以缓解这一问题，例如减少输入变量的数量、选择多种训练/测试集、设计隐藏层神经元数量较少的部分连接架构以及在训练过程中使用提前停止。通过在独立测试数据集上进行验证来评估所导出的ANN模型的可靠性。与传统的树木年轮气候学方法相比，ANN技术的主要优势在于其能够捕捉非线性的气候 - 生长响应，并且不依赖于预先假定的函数关系来描述观测数据集。本文介绍的ANN方法具有足够的通用性，可应用于许多森林生态建模应用。《林业科学》46(2):229 - 239
Modeling the nonlinear and complex relationships between climate and tree-ring growth is of significance in dendroclimatic studies, but difficult to implement using traditional linear regression approaches. To overcome this difficulty, the technique of Artificial Neural Network (ANN) was employed in this study to develop the growth response models using the climate/growth database for Douglas-fir (Pseudotsuga menziesii var. menziesii [Mirb.] Franco) on southern Vancouver Island, Canada. The results show that the ANN models are able to extract nonlinear growth response patterns from the observed climate/tree-ring datasets, and to generate more accurate predictions than multiple linear regression approaches. The ANN-extracted climate-growth relationships can be displayed by scenario analysis; for example, when all other input variables are held fixed at their means, the limiting effect of April-July precipitation on tree growth decreases with increased precipitation. The main difficulty of applying ANN technique in dendroclimatology is the problem of overlearning (i.e., the ANN learns too many specific climate-growth patterns and loses the ability to generalize between similar climate-growth patterns). This problem can be alleviated by carefully designing the ANN, such as reducing the number of input variables, choosing a variety of training/testing sets, designing partially connected architectures with a small number of neurons in the hidden layer, and using early stopping during training process. The reliability of the derived ANN models is assessed by validation on independent testing datasets. The main advantages of the ANN technique over traditional dendroclimatic approaches are its ability to capture nonlinear climate-growth response, and its nonreliance on preassumed functional relationships for describing the observed datasets. The ANN method introduced in this article is sufficiently general to be applicable to many forest ecological modeling applications. For. Sci. 46(2):229-239.