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OA

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.