基于深度学习的双阶段大坝变形预测模型

Two-Stage Dam Deformation Prediction Model Based on Deep Learning

为提高大坝位移预测的准确性,提出了一种新颖的基于深度学习的综合预测方法。首先引入了一种基于完全集成经验模态分解(Complete Ensemble Empirical Mode Decomposition with Adaptive Noise,CEEMDAN)和奇异谱分析(Singular Spectrum Analysis,SSA)的多级数据降噪技术。这能有效地消除监测数据中的噪声和异常值,提高数据质量,为后续预测提供更合理的大坝变形数据。随后构建基于卷积神经网络(Convolutional Neural Network,CNN)和门控循环单元(Gated Recurrent Unit,GRU)的深度学习模型。采用CNN从监测数据中提取丰富的特征,利用GRU来捕获和处理时间序列数据中的长期依赖关系。为了增强模型的表现,引入了自注意力机制,以帮助模型更好地处理和识别数据中的复杂模式。通过与其他预测方法的比较,实验结果表明,该方法在大坝位移预测的准确性和稳定性方面相较于其他方法有显著的提升,能够为大坝变形监控领域提供新方法。

In order to improve the accuracy of dam displacement prediction,this paper proposes a novel comprehensive prediction method based on deep learning.Initially,a multi-level data denoising technology based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)and Singular Spectrum Analysis(SSA)is introduced.This effectively eliminates the noise and outliers in the monitoring data,improving data quality,and providing more reasonable dam deformation data for subsequent predictions.Subsequently,a deep learning model based on Convolutional Neural Networks(CNN)and Gated Recurrent Units(GRU)is constructed.The CNN is used to extract rich features from the monitoring data,and the GRU is utilized to capture and process the long-term dependencies in the time series data.To enhance the model’s performance,a self-attention mechanism is introduced to help the model better handle and recognize complex patterns in the data.Compared with other prediction methods,experimental results show that this method significantly improves the accuracy and stability of dam displacement predictions,providing a new approach for the field of dam deformation monitoring.