三峡大学学报（自然科学版）2026，Vol.48Issue(4)：17-24,8.DOI:10.13393/j.cnki.issn.1672-948X.2026.04.003
基于IWOA-BiLSTM的高拱坝测点群变形预测时空模型
IWOA-BiLSTM-Based Deformation Prediction Method for Observation Point Group of In-Service High Arch Dam
摘要
Abstract
In view of the limits of the spatiotemporal model in the deformation prediction of the observation point groups of high arch dam,a novel method for the deformation prediction of observation point group is proposed by combining locally linear embedding(LLE),bidirectional long short-term memory network(BiLSTM),and whale optimization algorithm(WOA).LLE is used for factor dimension reduction,and the input-output relationship of BiLSTM is established.WOA is improved by introducing a nonlinear convergence factor,adaptive weights,Gaussian mutation disturbance,and Tent chaotic disturbance,so as to optimize the hyperparameters of BiLSTM.Subsequently,IWOA-BiLSTM-based deformation prediction model of observation point group is established.A case study is conducted to validate the proposed methodology.The results indicate that:Compared with WOA,the optimization ability,calculation stability,and convergence speed of IWOA are better.The multiple correlation coefficient and residual standard deviation of IWOA-BiLSTM in deformation prediction are 0.989 1 and 0.789 1,respectively,showing the best performance compared with WOA-BiLSTM,BiLSTM,and spatiotemporal model.关键词
测点群/因子降维/双向长短期记忆网络/鲸鱼优化算法/变形预测Key words
observation point group/factor dimension reduction/bidirectional long short-term memory network/whale optimization algorithm/deformation prediction分类
建筑与水利引用本文复制引用
杨光,黄嘉辉,赵阿辉,王琳,杨浩宇,贺习恒..基于IWOA-BiLSTM的高拱坝测点群变形预测时空模型[J].三峡大学学报（自然科学版）,2026,48(4):17-24,8.基金项目
国家自然科学基金项目(52109155,42401319) （52109155,42401319）
河南省自然科学基金面上项目(262300420052) （262300420052）
河南省重点研发与推广专项项目(262102320049) （262102320049）
水灾害防御全国重点实验室开放基金项目(2024491911) （2024491911）
河南省研究生教育改革与质量提升工程项目(YJS2026ALPY01) （YJS2026ALPY01）
华北水利水电大学学科建设与发展研究项目(培育项目23) （培育项目23）
