水力发电学报2026，Vol.45Issue(4)：27-42,16.DOI:10.11660/slfdxb.20260403
一种基于空间特征融合与多测点协同的变形耦合预测模型
Deformation-coupled prediction model based on spatial feature fusion and multi-point collaboration
摘要
Abstract
This study develops a collaborative multi-point ensemble forecasting framework to address the limitations of conventional dam-deformation prediction models—relying on isolated single-point data and thereby failing to simultaneously account for multi-source environmental factors and spatial synergistic effects.First,we use a correlation variation-based CA-KMeans clustering algorithm to partition the dam's monitoring stations into several sub-clusters by their temporal deformation characteristics and spatial positions,so as to enhance inter-point synergy.And,we construct a high-dimensional spatiotemporal feature matrix(HST-M)by integrating multi-source influencing factors-such as water pressure,temperature,time-dependent effects,and spatial coordinates-to characterize the dam's overall deformation behavior comprehensively.Then,an autoencoder is adopted to perform dimensionality reduction and feature refinement on these high-dimensional inputs,automatically extracting critical nonlinear correlations while suppressing redundancy.Finally,ridge regression serves as the predictor,leveraging its L2 regularization to deliver stable,well-generalized deformation forecasts even under high-dimensional,multicollinear conditions.Case studies demonstrate our new framework not only enhances predictive accuracy and robustness but offers high applicability and computational efficiency.关键词
大坝/变形预测/CA-Kmeans/高维时空特征/自编码器/岭回归Key words
dam/deformation prediction/CA-Kmeans/high-dimensional spatiotemporal features/autoencoder/ridge regression分类
建筑与水利引用本文复制引用
金申乐,杨平荣,胡超,谭立伟,柳聪聪,甘孝清..一种基于空间特征融合与多测点协同的变形耦合预测模型[J].水力发电学报,2026,45(4):27-42,16.基金项目
国家重点研发计划(2022YFC3005503) （2022YFC3005503）
中国中铁股份有限公司科技研究开发计划项目(2023-重大-16) （2023-重大-16）
