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Diameter distribution prediction of populus shelterbelts based on artificial neural network

机译:基于人工神经网络的杨树防护林直径分布预测

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Diameter distribution is used to predict stand stock, timber volume and stand yield in most forest management. In the paper, opulus shelterbelts in Boai County were analyzed. A model to predict stand diameter distribution was constructed with artificial neural network(ANN) approach by using the average stand diameter, the coefficient of variation of diameter as well as relative diameter as input variables, and cumulative frequency of tree number as output variables. The structure of the optimum model was 3∶11∶1 and the total fitting accuracy was 98.18 %. With respect to the model, the max, min and average fitting accuracy of the accumulated frequency could be calculated, which is 99.93 %, 88.48 % and 98.20 %. The corresponding prediction accuracy was 99.70 %, 94.36 % and 97.56 %, which has a similar characteristic to that of fitting accuracy. As in forestry practice, a model whose average accuracy is 95% is reliable enough to meet the practice request. Consequently, it can be concluded that the model works quite well and ANN approach can be used to perform the nonlinear systems such as the complicated stand diameter distribution.
机译:在大多数森林经营中,直径分布用于预测林分蓄积量,木材量和林分产量。本文分析了博爱县的杨树防护林带。以平均林分直径,直径和相对直径的变化系数作为输入变量,以树数的累积频率作为输出变量,采用人工神经网络(ANN)方法建立了预测林分直径分布的模型。最优模型的结构为3∶11∶1,总拟合精度为98.18%。对于该模型,可以计算出累积频率的最大,最小和平均拟合精度,分别为99.93%,88.48%和98.20%。相应的预测精度为99.70%,94.36%和97.56%,具有与拟合精度相似的特征。与林业实践一样,平均准确度为95%的模型足够可靠,可以满足实践要求。因此,可以得出结论,该模型运行良好,并且可以使用ANN方法执行非线性系统,例如复杂的机架直径分布。

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