基于BP神经网络的马尾松人工林胸径-树高模型预测

  目的  马尾松Pinus massoniana是中国南方主要用材树种，建立高效的马尾松人工林胸径-树高预测模型，可为马尾松人工林经营提供理论指导。  方法  以贵州省黔中地区马尾松人工林为研究对象，基于82块样地(25 m×25 m)的4 284株马尾松单木数据，选取6个常用的广义非线性模型进行拟合，从中筛选出拟合效果最好的模型。使用相同的数据确定最佳隐层节点数量后，经过反复训练建立基于BP神经网络的马尾松胸径-树高预测模型。  结果  在6个广义非线性模型中，拟合效果最佳为Korf模型(R2=0.650)；马尾松适宜的隐藏层节点数为2，适宜的模型结构(输入层节点数∶隐藏层节点数∶输出层节点数)为1∶2∶1，模型预测精度达0.717。  结论  广义非线性模型能较好地拟合马尾松人工林胸径-树高关系，但与BP神经网络模型相比，BP神经网络不需要依赖经验模型，也不用模型筛选，而且BP神经网络模型具有较高的决定系数和较低的均方根误差，拟合精度优于广义非线性模型。图5表5参35

Diameter-height model for Pinus massoniana plantations based on BP neural network

  Objective  To provide theoretical guidance for the management of the plantation of Pinus massoniana, a wood widely used in the south, this study is aimed at the establishment of a high-efficiency diameter-height Model.  Method  With 4 284 P. massoniana plants from 82 sample plots in central Guizhou Province as the study subjects, 6 generalized nonlinear models were selected to fit the diameter-height relationship. After the determination of the optimal number of hidden layer nodes, repeated training was conducted to establish the BP neural network-based model of diameter-height relationship for P. massoniana plantation.  Result  Among the 6 generalized nonlinear models, the model that fits the best is Korf function (R2=0.650) while the two hidden layers for BP neural network model were suitable for P. massoniana plantation with 1∶2∶1 as the appropriate model structure (number of input layer nodes∶number of hidden layer nodes∶number of output layer nodes) was and the model prediction accuracy reaching 0.717.  Conclusion  The generalized nonlinear model can fit the diameter-height relationship of P. massoniana plantation pretty well, yet in comparison, the BP neural network works better in that it’s not reliant on empirical models and has better fitting accuracy for the high R2 yet lower root mean squared error. [Ch, 5 fig. 5 tab. 35 ref.]