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Updated: Jul 13, 2025

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关于用不同因素选方法和随机森林模型绘制山体滑坡易感性图的研究

Tengfei Gu1,2, Jia Li1, Mingguo Wang3

  • 1Faculty of Geography, Yunnan Normal University, Kunming, Yunnan Province, China.

PloS one
|October 12, 2023
PubMed
概括

因子选显著提高了山体滑坡易感性测绘 (LSM) 模型的准确性. 信息获取比率 (IGR) 方法与随机森林 (RF) 模型相结合,产生了最好的预测性能,突出了其用于滑坡风险评估的实用性.

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The process of RSM involves several key steps:
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Strategies for Assessing and Addressing Confounding

Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
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Survival Tree

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.
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Hazard Rate01:11

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