水力发电学报2026，Vol.45Issue(2)：1-14,14.DOI:10.11660/slfdxb.20260201
融合特征选择与特征提取的带缝拱坝位移预测模型
Displacement prediction model for arch dams with cracks integrating feature selection and feature extraction
摘要
Abstract
Previous prediction models were limited by their inadequate consideration of temperature hysteresis effects and crack influences of an arch dam,and suffer from overly complex,redundant displacement factors and low prediction accuracy.To achieve accurate predictions of displacement in the arch dams with significant cracks,this paper develops a novel predictive method.First,we construct a displacement monitoring model for the dams,accounting for temperature hysteresis effect and crack influences.Then,a gradient boosting regression tree(GBRT)is used for feature selection among influencing factors,eliminating irrelevant variables;Kernel principal component analysis(KPCA)is applied to extract features from the retained temperature hysteresis and crack factors,so as to construct a displacement prediction dataset.Finally,we construct a displacement prediction model by integrating the salp swarm algorithm with the kernel extreme learning machine(SSA-KELM).Engineering case results demonstrate feature selection and feature extraction effectively mitigate the interference of irrelevant variables and reduce data dimensions,thereby improving prediction accuracy significantly.Compared with other benchmark models,SSA-KELM that presents the highest prediction accuracy and stability is a new viable approach for predicting displacement in arch dams with cracks.关键词
拱坝位移预测/滞后效应/裂缝影响/特征选择/核主成分分析/机器学习Key words
arch dam displacement prediction/hysteresis effect/crack influence/feature selection/kernel principal component analysis/machine learning分类
建筑与水利引用本文复制引用
蒋成阳,苏怀智,徐波..融合特征选择与特征提取的带缝拱坝位移预测模型[J].水力发电学报,2026,45(2):1-14,14.基金项目
国家自然科学基金项目(52079120) （52079120）
水灾害防御全国重点实验室开放基金项目(2024490211) （2024490211）
