Applications of machine learning algorithms in forest growth and yield prediction.
Applications of machine learning algorithms in forest growth and yield prediction.
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DOI：
10.12171/j.1000-1522.20190356
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发表时间：
2019-01-01
期刊：
影响因子：
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通讯作者：
Lei, X. D.
中科院分区：
文献类型：
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作者：
Lei, Xiang-dong;Lei, X. D.
森林生长与收获预测是森林经营科学的重要研究领域，森林生长与收获模型的建立是森林经营决策的关键。传统的统计增长模型，如线性和非线性回归模型、混合效应模型、分位数回归、误差变量模型等，都是在一定的统计假设条件下应用的，如数据是独立的、正态分布的和同方差的。对于具有重复观测和层次性的森林数据，通常难以满足上述要求。人工智能技术的发展为森林生长建模提供了一种新的方法，它具有对数据分布无要求、从数据中提取深层知识、精度高等优点。在森林中的应用。增长和产量仍然低于其他领域。综述了分类回归树（CART）、多元自适应回归样条（MARS）、Bagging回归、Boosted回归树（EMT）、随机森林（RP）、人工神经网络（ANN）、k-近邻（k-NN）和支持向量机（SVM）等主要机器学习算法的参数整定、软件、优势和挑战。机器学习在森林生长和产量预测中具有广阔的应用前景，与传统统计方法相结合将成为森林生长和产量预测的发展趋势。关键词：森林生长与收获预测;回归;分类;机器学习算法。
Forest growth and yield prediction is an important field of forest management science, and modelling forest growth and yield is key to forest management decision-making. The traditional statistical growth models such as linear and nonlinear regression model, mixed-effect model, quantile regression, variable-in-error model are often applied under certain statistical assumptions, such as the data are independent, normally distributed and homoscedastic. The above requirements are usually difficult to be met for forest data with repeated observation and hierarchy. With the development of Al techniques, machine teaming provides a new way for forest growth modeling, with the advantages of no requirements on data distribution, extracting deep knowledge from the data, and high accuracy. The applications in forest. growth and yield are still less than other domains. We reviewed the main machine learning algorithms including classification and regression tree (CART), multivariate adaptive regression splines (MARS), bagging regression, boosted regression tree (EMT), random forest (RP), artificial neural networks (ANN), k-nearest neighbors (k-NN), and support vector machine (SVM), parameter tuning, software, advantages and challenge. We conclude that machine learning would be widely applied with great potential and its combination with traditional statistical methods would become a trend in forest growth and yield prediction. Key words: forest growth and yield prediction; regression; classification; machine learning algorithm.