水力发电2025，Vol.51Issue(4)：97-103,110,8.
基于改进聚类方法的大坝安全监测算法
Improved Clustering-Based Algorithm for Dam Safety Monitoring
摘要
Abstract
To enhance the accuracy and efficiency of outlier detection and risk prediction in dam safety forecasting,the K-Shape clustering method is improved by incorporating spatial distance into the evaluation metrics,which refines the time series clustering algorithm used for dam monitoring,and additionally,the clustering center line is integrated with statistical modeling techniques to develop a method for outlier identification based on clustering results.By leveraging the clustering outcomes and utilizing Long Short-Term Memory(LSTM)network,the issues of outlier detection and missing value imputation are effectively addressed,and the prediction of trends in dam operational behavior is enabled.This method is applied to the safety monitoring of a dam in Yunnan Province for the tests and predictions on the spatiotemporal sequences of displacement and temperature monitoring data.The results indicate that the spatial distance significantly influences clustering outcomes,and the improved clustering algorithm demonstrates enhanced performance in outlier detection compared to traditional methods.Furthermore,the multi-point prediction algorithm exhibits higher accuracy,confirming the practical value of incorporating spatial distance into clustering algorithms.关键词
大坝安全监测/精度/时空序列聚类/K-Shape聚类方法/长短期记忆网络(LSTM)Key words
dam safety monitoring/accuracy/spatiotemporal series clustering/K-Shape clustering method/Long Short-Term Memory(LSTM)分类
水利科学引用本文复制引用
李东明,聂一丹,晁阳,齐慧君,林潮宁..基于改进聚类方法的大坝安全监测算法[J].水力发电,2025,51(4):97-103,110,8.基金项目
国家重点研发计划(2022YFC3005403) （2022YFC3005403）
国家自然科学基金资助项目(52309151) （52309151）
水利部水库大坝安全重点实验室开放研究基金(YK323007) （YK323007）
