相关实验视频

Updated: Jan 9, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

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机器学习组合概率方法用于在快速水位下降的情况下对水库斜坡的时间依赖性可靠性分析,使用贝叶斯模型平均 (BMA) 计算

Zehang Li1, Zhu Yang2,3, Long Teng2,3

  • 1School of Civil Engineering, Architecture and Environment, Hubei University of Technology, 28 Nanli Road, Wuhan, 430068, People's Republic of China.

Scientific reports
|November 29, 2025
PubMed
概括

这项研究引入了机器学习 (ML) 组合的概率方法,使用贝叶斯模型平均 (BMA) 来改进水库坡度稳定性分析. 该方法提高了故障概率估计,并为时间依赖的可靠性量化预测不确定性.

关键词:
贝叶斯模型的平均值是贝叶斯的模型.机器学习是机器学习.预测不确定性 预测不确定性储水池的坡度倾斜的情况时间依赖的可靠性.

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