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预测本托尼特膨胀压力:优化的XGBoost与神经网络相比

Utkarsh1, Pradeep Kumar Jain2

  • 1Department of Civil Engineering, Maulana Azad National Institute of Technology, Bhopal, 462003, India. amanutkarsh619@gmail.com.

Scientific reports
|July 30, 2024
PubMed
概括

一个新的机器学习模型准确地预测了放射性废物存储库的顿岩膨胀压力. 这种进步提高了关键地质屏障系统的稳定性和密封性.

关键词:
托尼特 (Bentonite) 是一种托尼特.优化优化 优化优化柔软的土壤 柔软的土壤膨胀压力压力的膨胀极端的梯度增强可以提升.

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相关概念视频

Survival Tree01:19

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.
 Building a Survival Tree
Constructing a survival tree begins...