水力发电2024，Vol.50Issue(6)：67-71,5.
时间序列重构改进LSTM的大坝变形预测模型
Dam Deformation Prediction Model Based on Time Series Reconstruction and Improved LSTM
摘要
Abstract
Dam deformation is usually highly volatile,and traditional methods often fail to capture nonlinear relationships well,which in turn affects prediction accuracy.This paper proposes a dam time-series deformation prediction framework based on Singular Value Decomposition(SVD)and Long Short-term Memory Neural Network(LSTM),aiming to improve the prediction accuracy of dam deformation.First,the original deformation sequence is decomposed into a series of more regular subsequences by the construction of Hankel matrix.Then the corresponding LSTM models are built for each component.Finally,the output sequences of each model are reconstructed to obtain the final deformation prediction values.Analysis shows that the SVD method can effectively reduce the nonlinearity of original sequence,while the LSTM can effectively capture the nonlinear relationship of time series and obtain satisfactory prediction results.Compared with traditional methods,the prediction performance of SVD-LSTM is optimal,which provides a new idea for the construction of dam safety system.关键词
大坝变形预测/时间序列重构/奇异值分解/LSTM/非平稳Key words
dam deformation prediction/time series reconstruction/singular value decomposition/LSTM/non-stationary分类
建筑与水利引用本文复制引用
李相如,苏超,袁荣耀..时间序列重构改进LSTM的大坝变形预测模型[J].水力发电,2024,50(6):67-71,5.基金项目
国家自然科学基金资助项目(51579089) （51579089）
