水力发电学报2025，Vol.44Issue(9)：98-113,16.DOI:10.11660/slfdxb.20250909
CRITIC-Stacking集成学习在大坝安全监测数据缺失值处理中的应用
Application of CRITIC-Stacking ensemble learning in missing value processing of dam safety monitoring data
摘要
Abstract
Missing value processing is an important foundation for analysis of dam safety monitoring data.Traditional methods for handling the missing values of a dam often use a single type of machine learning models for prediction and interpolation,ineffective in integrating the advantages of multiple types of machine learning models.This article integrates multiple classic machine learning and deep learning algorithms into a strong learner within the framework of ensemble learning.To address the issue of weight allocation to each model,we develop a new critic stacking(CS)weight allocation method so that we can construct a dam monitoring data interpolation hybrid model based on CS ensemble learning.The results show that compared to single base learners and traditional Stacking ensemble models,this CRITIC-Stacking ensemble learning method reduces the RMSE index by an average of 72.7%and 58%.This indicates that the method can fully leverage the predictive advantages of various machine learning models,and the improvement of weight allocation can also improve the predictive accuracy of ensemble learning models,thus providing a new solution for handling missing values in dam monitoring data and constructing prediction models.关键词
大坝/安全监测/集成学习/缺失值处理/预测模型Key words
dam/safety monitoring/integrated learning/missing value handling/prediction model分类
建筑与水利引用本文复制引用
宋锦焘,董嘉磊,杨杰,程琳,葛佳豪..CRITIC-Stacking集成学习在大坝安全监测数据缺失值处理中的应用[J].水力发电学报,2025,44(9):98-113,16.基金项目
国家自然科学基金面上项目(52579135) （52579135）
国家自然科学基金重点项目(52039008) （52039008）
国家自然科学基金青年项目(52109166) （52109166）
