基于改进的LSTM方法进行土地表面沉积变形的预测模型:CEEMDAN-ICA-AM-LSTM (CIAL) 预测模型
Shengchao Zhu1, Yongjun Qin1,2, Xin Meng1
1College of Civil Engineering and Architecture, Xinjiang University, Urumqi, Xinjiang, China.
PloS one
|March 7, 2024
概括
从地铁建设中预测地面沉积对安全至关重要. 一个新的CEEMDAN-ICA-AM-LSTM模型准确地预测了表面沉积,改善了安全监测.
科学领域:
- 地质技术工程 地质技术工程
- 土木工程 土木工程是指土木工程.
- 机器学习在工程中的应用.
背景情况:
- 地铁建设导致复杂的地面解决,对财产和生命构成风险.
- 传统的预测方法与非线性,时间滞后和多种影响因素作斗争,无法满足行业的准确性需求.
- 实时预测表面沉积变形对于减轻建筑风险至关重要.
研究的目的:
- 开发一个先进的表面沉积变形预测模型.
- 为了提高地铁建设监测的预测准确性和可靠性.
- 为了解决捕捉复杂结算动态的传统方法的局限性.
主要方法:
- 提出了一个新的预测模型,将完整集体实证模式分解与自适应噪声 (CEEMDAN) 和独立组件分析 (ICA) 结合起来,用于数据无声化.
- 集成了长期短期记忆 (LSTM) 网络与注意力机制 (AM) 以提高预测能力.
- 开发了CEEMDAN-ICA-AM-LSTM (CIAL) 模型用于表面沉积变形预测.
主要成果:
- 与多个现有的预测模型相比,CIAL模型显示出更高的有效性和适用性.
- 实现了低误差指标:RMSE为0.041,MAE为0.033,MAPE为0.384%,其中R2值最高.
- 该模型在分析真实世界的结算数据方面表现出极好的预测准确性,最佳预测效果和良好的可靠性.
结论:
- 该CIAL模型在预测地铁建设造成的表面沉积变形方面取得了重大进展.
- 这种新方法为表面沉积提供了有效和可靠的安全监测.
- 降噪技术与先进的深度学习模型的整合提高了复杂地质技术现象的预测性能.
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