水力发电学报2026，Vol.45Issue(5)：80-94,15.DOI:10.11660/slfdxb.20260507
环境数据失真条件下土石坝变形预测方法研究
Deformation prediction method of earth rock dams under environmental data distortion conditions
摘要
Abstract
During the daily operation and management of an earth-rock dam,data monitoring often suffers from difficulties caused by environmental data distortion and even data sequence gaps or interruptions.Owing to this challenge,traditional prediction methods have struggled to conduct a scientific and reliable analysis of the dam's deformation behaviors and health conditions.This paper presents a novel prediction method for earth-rock dam deformation,based on a single-time-series optimization model and the multi-scale combination theory.We consider the strong time-dependence of these deformation behaviors,and construct a Transformer single-time-series training model that features an excellent capability of global dependency learning to capture the autocorrelation of target variables accurately.And,variational mode decomposition is adopted to implement frequency-domain preprocessing of the training samples to reduce cross-interference from multi-frequency components and noise within the data.Further,we use the Kepler optimization algorithm to optimize the decomposition parameters and the historical information volume input for sub-sequence training,and thereby achieve a deformation prediction model for earth-rock dams under the condition of environmental data distortion.Case studies demonstrate this new method presents satisfactory prediction performance and strong generalization capability of handling non-stationary and low-quality monitoring data,showing a promising potential for practical deformation analysis of earth-rock dams under complicated monitoring conditions.关键词
土石坝/变形监测/预测方法/环境数据失真/单时间序列模型Key words
earth-rock dam/deformation monitoring/prediction method/environmental data distortion/single-time-series model分类
建筑与水利引用本文复制引用
陈良捷,李萌,李焱,林太清,熊家归..环境数据失真条件下土石坝变形预测方法研究[J].水力发电学报,2026,45(5):80-94,15.基金项目
国家自然科学基金项目(52569023 （）
52469020) （）
江西省水利厅科技项目(202425YBKT05) （202425YBKT05）
