With the continuous construction of digital twin hydraulic engineering

safety monitoring

as the focus of data base construction

is the basis of subsequent professional models and business applica_ tions. As the abnormal data generated during the collection and transmission process would affect the re_ liability and integrity of data

identifying and clearing abnormal data is the key to improve the quality of monitoring data and ensure dam safety assessment. Therefore

the generalized additive model combined with isolated forest algorithm is applied in the identification of abnormal data of dam monitoring. Firstly

the obvious outliers in monitoring data are identified by isolated forest algorithm. Then

the trend items of monitoring data series are extracted by generalized addition model

and trend item information is elim_ inated. And the 3δ method is used for refined identificative of outliers. It is proved that the method can effectively identify the outliers in dam monitoring data through the example data analysis.