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Stand Diameter Distribution Modeling and Prediction Based on Maximum Entropy Principle

机译:基于最大熵原理的林分直径分布建模与预测

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Research Highlights: Improving the prediction accuracy represents a popular forest simulation modeling issue, and exploring the optimal maximum entropy (MaxEnt) distribution is a new effective method for improving the diameter distribution model simulation precision to overcome the disadvantages of Weibull. Background and Objectives: The MaxEnt distribution is the closest to the actual distribution under the constraints, which are the main probability density distributions. However, relatively few studies have addressed the optimization of stand diameter distribution based on MaxEnt distribution. The objective of this study was to introduce application of the MaxEnt distribution on modeling and prediction of stand diameter distribution. Materials and Methods: The long-term repeated measurement data sets consisted of 260 diameter frequency distributions from China fir ( Cunninghamia lanceolate (Lamb.) Hook) plantations in the southern China Guizhou. The Weibull distribution and the MaxEnt distribution were applied to the fitting of stand diameter distribution, and the modeling and prediction characteristics of Weibull distribution and MaxEnt distribution to stand diameter distribution were compared. Results: Three main conclusions were obtained: (1) MaxEnt distribution presented a more accurate simulation than three-parametric Weibull function; (2) the Chi-square test showed diameter distributions of unknown stands can be well estimated by applying MaxEnt distribution based on the plot similarity index method (PSIM) and Weibull distribution based on the parameter prediction method (PPM); (3) the MaxEnt model can deal with the complex nonlinear relationship and show strong prediction ability when predicting the stand distribution structure. Conclusions: With the increase of sample size, the PSIM has great application prospects in the dynamic prediction system of stand diameter distribution.
机译:研究重点:提高预测精度代表了一个流行的森林模拟建模问题,探索最佳最大熵(MaxEnt)分布是一种提高直径分布模型模拟精度以克服Weibull缺点的有效方法。背景和目标:在约束条件下,MaxEnt分布最接近实际分布,这是主要的概率密度分布。但是,很少有研究针对基于MaxEnt分布的机架直径分布进行优化。这项研究的目的是介绍MaxEnt分布在林分直径分布的建模和预测中的应用。材料和方法:长期重复测量数据集由贵州南部杉木(Cunninghamia lanceolate(Lamb。)Hook)人工林的260个直径频率分布组成。将Weibull分布和MaxEnt分布应用于林分直径分布的拟合,比较了Weibull分布和MaxEnt分布对林分直径的建模和预测特性。结果:获得了三个主要结论:(1)MaxEnt分布比三参数Weibull函数提供了更准确的仿真; (2)卡方检验表明,通过基于图相似度指数法(PSIM)的MaxEnt分布和基于参数预测方法(PPM)的威布尔分布,可以很好地估计未知林分的直径分布; (3)MaxEnt模型可以处理复杂的非线性关系,在预测林分分布结构时显示出强大的预测能力。结论:随着样本量的增加,PSIM在林分直径动态预测系统中具有广阔的应用前景。

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