Abnormal Diagnosis of Dam Safety Monitoring Data Based on Ensemble Learning

Screening out the gross errors and systematic errors of dam safety monitoring data by theoretical hypothesis will lead to the risk of misjudgment of abnormal data. In order to reduce this risk, based on the ensemble learning method in machine learning, this article extracts and integrates multiple b...

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Bibliographic Details
Published in:IOP conference series. Earth and environmental science 2019-05, Vol.267 (6), p.62027
Main Authors: Jun, Zhang, Jiemin, Xie, Pangao, Kou
Format: Article
Language:English
Subjects:
Dam safety
Dams
Diagnosis
Ensemble learning
Learning algorithms
Machine learning
Monitoring
Regression models
Risk reduction
Systematic errors
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Summary:Screening out the gross errors and systematic errors of dam safety monitoring data by theoretical hypothesis will lead to the risk of misjudgment of abnormal data. In order to reduce this risk, based on the ensemble learning method in machine learning, this article extracts and integrates multiple base learners from the stepwise regression model, and proposes a matrix of abnormal indexes based on real-time data update, and analyzes the abnormal diagnosis of the measured data subsequently. The results show that the abnormal indexes have a strong practicability, which don't need to screen out the data with systematic errors and gross errors, and can effectively identify the abnormal time points and the degree of interference between the measured values.
ISSN:1755-1307
1755-1315
DOI:10.1088/1755-1315/267/6/062027