一种基于CNN-LSTM注意力的漏压力预测方法,用于地球和岩石水
Hanqiu Chen1, Kui Wang2, Mingjie Zhao3
1Engineering Research Center of Diagnosis Technology and Instruments of Hydro-Construction, Chongqing Jiaotong University, Chongqing, 400074, China.
Scientific reports
|April 15, 2025
概括
这项研究引入了一种使用卷积神经网络 (CNN) 和长期短期记忆 (LSTM) 网络的新方法,该网络具有注意力机制,用于预测水中的漏压力. 先进的模型显著提高了风险管理的准确性和稳定性.
科学领域:
- 地质技术工程 地质技术工程
- 人工智能的人工智能
- 土木工程 土木工程是指土木工程.
背景情况:
- 在土壤和岩石水中,漏压力监测对于风险管理至关重要.
- 现有的预测模型与非线性作斗争,缺乏准确性和通用性.
研究的目的:
- 开发一种先进的方法来预测土壤和岩石水的漏压力.
- 解决当前模型在捕捉复杂影响因素方面的局限性.
主要方法:
- 一种混合方法,将卷积神经网络 (CNN) 用于特征提取和长短期记忆 (LSTM) 网络用于时间序列数据分析.
- 整合注意力机制,专注于关键数据点,增强预测.
- 在模型应用之前进行数据规范化和数据集分割.
主要成果:
- 与单个CNN-LSTM,LSTM,变压器和BP模型相比,提出的带有注意力机制的CNN-LSTM表现出更高的性能.
- 该方法在测量点实现了最小的平均绝对误差 (MAE),平均绝对百分比误差 (MAPE) 和根平均平方误差 (RMSE).
- 该模型有效地捕获了漏压力数据的局部变化特征,显示了增强的稳定性.
结论:
- 开发的方法提供了一种新且准确的方法,用于预测水中的漏压力.
- 模型的增强精度,稳定性和稳定性对于地质工程中的有效风险评估和管理至关重要.
- 这种由人工智能驱动的方法克服了传统模型在处理复杂的漏动态方面的局限性.
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