一种改进的GRU方法用于斜坡应力预测
Lichun Bai1,2, Ronghui Zhao3, Sen Lin4
1Ordos Institute of Liaoning Technical University, Ordos, 017004, China.
Scientific reports
|April 21, 2025
概括
一个新的智能模型,变化模式分解 (VMD) 与泥虫优化 (DBO) 和改进的门式循环单元 (GRU),提高了对露天矿山斜坡的山体滑坡预测准确度. 这种VMD-DBO-GRU-A模型显著改善了预警系统.
科学领域:
- 地质技术工程 地质技术工程
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 露天矿山坡度稳定性是一个由应力变化影响的复杂非线性系统.
- 准确的山体滑坡预测和早期预警对于风险评估至关重要.
- 传统方法在非线性时间序列数据方面存在困难,其准确性和稳定性较低.
研究的目的:
- 为开放式矿山斜坡应力数据开发一个智能预测模型.
- 为了提高山体滑坡预警系统的准确性和稳定性.
- 解决传统预测方法在处理非线性时间序列数据方面的局限性.
主要方法:
- 变化模式分解 (VMD) 用于压力数据的预处理和特征提取.
- 泥虫优化 (DBO) 优化门式反复单位 (GRU) 参数 (隐藏层,学习率).
- 在GRU内集成自我注意机制,以增强功能相关性捕获.
主要成果:
- VMD-DBO-GRU-A模型显示,平方根平均误差显著减少 (77%与LSTM相比,84%与SVM相比).
- 获得了高的确定系数 (0.9978),表明了出色的预测性能.
- 该模型有效地提取本地特征并捕获内部数据的相关性.
结论:
- VMD-DBO-GRU-A模型提供了卓越的性能和高预测准确性,用于露天矿山斜坡稳定性.
- 这种智能模型为实际的山体滑坡预警应用提供了宝贵的进步.
- 优化的GRU与VMD预处理和自我注意力是有效的复杂的非线性时间序列分析.
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