水力发电2025，Vol.51Issue(3)：113-118,6.
考虑子序列特征的大坝位移组合预测模型
Multi-scale Dam Displacement Prediction Model Considering Subsequence Characteristics
摘要
Abstract
A multi-scale prediction model of dam displacement based on singular spectrum analysis and Bagging ensemble learning is proposed to solve the problem of single prediction mode,insufficient generalization ability and easy deviation when processing the subsequences of multi-scale analysis model.Firstly,the measured data is decomposed by singular value decomposition to obtain subsequences such as trend term and period term,and considering that the period term is highly dependent on time,the period term is forecasted as a single time series to eliminate the influence of non-critical factors.Secondly,the Bagging ensemble learning,support vector machine and random forest model are used to construct a combined prediction model of dam displacement.On this basis,the predicted results of the trend term and the period term are added up to get the predicted results of dam displacement.The application example shows that the model can fully explore the physical mechanism of trend and periodic change contained in measured data,and provides a new idea for the long-term service behavior diagnosis of dams.关键词
多尺度/奇异谱分析/Bagging集成学习/大坝位移预测Key words
multi-scale/singular spectrum analysis/Bagging ensemble learning/dam displacement prediction分类
建筑与水利引用本文复制引用
林宏恩,赵二峰,刘峰,宋桂华..考虑子序列特征的大坝位移组合预测模型[J].水力发电,2025,51(3):113-118,6.基金项目
国家自然科学基金资助项目(52079046) （52079046）
云南省水利水电工程安全重点实验室开放课题基金(202302AN360003) （202302AN360003）
上海勘测设计研究院有限公司科研项目(2021SD(8)-2009) （2021SD(8）
