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相关概念视频

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.
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相关实验视频

Updated: Jun 27, 2025

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使用深度学习进行滑坡易感性评估,考虑不平衡的样本分布.

Deborah Simon Mwakapesa1, Xiaoji Lan1, Yimin Mao2,3

  • 1School of Civil, and Surveying, & Mapping Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China.

Heliyon
|May 6, 2024
PubMed
概括

本研究引入了一种深度学习方法 (DNN-MSFM),通过有效处理不平衡数据来改善山体滑坡易感性评估 (LSA). 新方法显著提高了地质灾害管理的预测准确性.

关键词:
DNN DNN 在线深度学习是一种深度学习.土地滑坡易感性的评估评估损失函数是一个损失函数.不平衡的数据不平衡的数据.

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科学领域:

  • 地质科学 地质科学
  • 人工智能的人工智能
  • 地质灾害管理地质灾害管理

背景情况:

  • 土地滑坡易感性评估 (LSA) 对于减轻地质灾害至关重要.
  • 传统的LSA模型在与不平衡的数据集作斗争,在这些数据集中,山体滑坡的发生频率低于非山体滑坡地区.
  • 深度学习有潜力,但需要方法来解决数据不平衡.

研究的目的:

  • 开发和评估一种新的深度学习方法 (DNN-MSFM) 来增强LSA.
  • 为了应对LSA数据集中样本分布不平衡的挑战.
  • 提高滑坡易感性预测的准确性和可靠性.

主要方法:

  • 一个深度神经网络 (DNN) 模型与一个平均平方错误分类损失函数 (MSFM) 结合起来.
  • DNN-MSFM方法的设计是为了算法处理不平衡的样本.
  • 模型性能使用统计指标 (总准确度,AUC) 进行评估,并与真实数据集上的DNN和支持矢量机 (SVM) 模型进行比较.

主要成果:

  • 该DNN-MSFM模型实现了0.889的高整体精度和0.84.84的AUC.
  • 与基线DNN和SVM模型相比,观察到显著的性能提升.
  • 该模型在学习山体滑坡易感性特征方面表现出有效性,并在不平衡数据上提供了改进的预测.

结论:

  • 对于LSA,DNN-MSFM方法是有效的,特别是在失衡的山体滑坡样本数据的场景中.
  • 该研究强调了平衡损失函数在训练LSA的深度神经网络中的重要性.
  • 这项研究有助于推进地质灾害评估和管理中的深度学习应用.