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贝叶斯启发的动态滞后因果图和角色意识变压器用于预测山体滑坡移位.

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  • 1School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China.

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概括
此摘要是机器生成的。

一个新的因果作用感知变压器 (CRAFormer) 通过从降雨数据中学习因果关系来改善山体滑坡移位预测. 这种方法增强了因降雨引起的山体滑坡的预警系统,大大减少了预测错误.

关键词:
一天一天的时间.因果发现的发现.动态贝叶斯网络 是一个贝叶斯网络.预测山体滑坡的移位情况降雨引起的山体滑坡造成的山体滑坡

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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
  • 地震工程的工程是地震工程.

背景情况:

  • 强烈的降雨正在增加山体滑坡的频率和风险,特别是在坡和薄土壤的地区,如中国南部.
  • 由于复杂的多阶段管道,现有的山体滑坡位移预测方法往往难以跨域转移和适应性.
  • 对于有效的早期预警系统来说,跨多种变形模式的准确预测至关重要.

研究的目的:

  • 为早期预警系统开发一种新,适应性强,准确的滑坡位移预测方法.
  • 解决跨领域转移和适应性现有方法的局限性.
  • 为了利用因果发现,更好地预测降雨引起的山体滑坡.

主要方法:

  • 开发了CRAFormer,一个由动态滞后贝叶斯网络风格因果图指导的因果角色意识的变压器模型.
  • 利用发现的定向非循环图 (DAG) 将驱动器分为因果作用,并为编码器创建角色特定的,非预测性面具.
  • 实施了环境意识的Top-2门,用于分支输出的稀疏融合,并纳入了无泄漏的ICS尾巴,用于外源降雨预测.

主要成果:

  • 与最强的基线相比,CRAFormer在各站减少了59-79%的平均绝对误差 (MAE) 和根平均平方误差 (RMSE).
  • 该模型在转折点和步骤事件附近展示了改进的准确性,这是山体滑坡的特征.
  • 在使用精心策划的GNSS数据集的广西两个对比的山体滑坡地点 (LaMenTun和BaYiTun) 的表现强.

结论:

  • 因果发现与神经预测相结合,为降雨引起的山体滑坡预测提供了一个实用的方法.
  • CRAFormer显示了通过准确和适应性的移位预测来增强早期预警系统的巨大潜力.
  • 该研究验证了因果面具,无泄漏的ICS尾巴和单调性先验在提高预测准确性的贡献.