Algorithm for Detecting Abnormal Data of Dam Monitoring Based on IF-Encoder
The outliers in dam safety monitoring data can have an impact on the correctness and timeliness of dam safety analysis and deci-sion-making.In order to accurately and efficiently detect outliers in dam safety monitoring data,an anomaly data detection algorithm based on IF Encoder was proposed.The target sequence was rebuilt based on the correlation between time series,and the residual size between the re-built sequence and the target sequence was compared to identify outliers.In addition,according to regulatory requirements,a correlation based outlier identification method was proposed,which divided the detected outliers into true and false anomalies,and removed false anoma-lies while retaining the true outliers.The results show that compared with the quartile method,Rayda criterion,KNN nearest neighbor meth-od,and DBSCAN clustering method,the IF-Encoder algorithm has improved recall,precision,and accuracy in detecting outliers,and its recognition of outliers is more reliable and effective.The correlation based outlier identification method has an accuracy rate of 92%for identifying true anomalies and 100%for identifying false anomalies,which can effectively classify outliers.
Isolated Forestsoutlier detectioncorrelationConvolutional Long Short Term Neural Networks
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