There widely exists anomalous data in dam monitoring.Identifying and removing anomalous data contrib-utes to maintaining the stability and reliability of models,thereby enhancing their predictive or classification performance.At the same time,real-time monitoring and anomaly detection can ensure the safe operation of systems.This paper intro-duces the Angle-Based Outlier Detection(ABOD)algorithm for identifying anomalous data in dam monitoring.Firstly,Empirical Mode Decomposition(EMD)was used to extract the high-frequency intrinsic mode functions of monitoring da-ta.Subsequently,anomaly detection is performed on the new dataset composed of these high-frequency intrinsic mode functions.Applied to data from Changheba,through comparative analysis with other methods,EMD-ABOD demon-strates an effective enhancement in the accuracy of anomalous data identification.
维普
万方数据