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Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
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一种机器学习方法来填补树仪数据中的空白.

Eileen Kuhl1, Emanuele Ziaco1, Jan Esper1,2

  • 1Department of Geography, Johannes Gutenberg University, Johann-Joachim-Becher Weg 32, 55128 Mainz, Germany.

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概括

极端梯度增强 (XGB) 有效地填补了单个树木树仪记录中的长时间数据空白. 这种机器学习方法不需要额外的树木或气候数据,为时间序列分析提供了强大的解决方案.

关键词:
在Acer中,有金化物.牙生态学 牙生态学计入计算是指计入计算的方法.普拉塔努斯 (Platanus) 与西班牙人 (Hispanica) 的比赛树木的生长 树木的生长城市树木 城市树木

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科学领域:

  • 登德罗年代学和生态监测.
  • 机器学习在环境科学中的应用.

背景情况:

  • 树仪数据的时间序列分析经常受到设备故障造成的数据缺口的阻碍.
  • 现有的填补差距方法有局限性,包括依赖较小的差距,外部树数据或气候参数.

研究的目的:

  • 评估机器学习算法,以填补单个树木树仪数据中的空白.
  • 找出最有效的算法来弥合数据缺口,而无需外部依赖.

主要方法:

  • 测试八个机器学习算法在城市和非城市树木的树枝计数据上.
  • 利用人工创建的空白来评估算法性能.
  • 专注于填补单个树木的空白的能力.

主要成果:

  • 极端梯度增强 (XGB) 在弥合人工数据差距方面表现出最高的技能.
  • XGB模型成功填补了连续30天的空白,在生长季节的开始和结束时表现良好.
  • 该方法证明独立于气候变量和邻近树木的数据.

结论:

  • 机器学习,特别是XGB,提供了一种有效和有效的方法来填补单个树木树仪数据中的空白.
  • 这种方法在树际数据相关性有限的场景中特别有价值,例如城市环境.
  • 这些发现支持使用ML来提高树枝生态时间序列分析的稳定性.