Abstract: In order to solve the problems of fuzzle and uncertainty in the processing results of boundary position data by isolated forest (IF) algorithm in the process of outlier identification,and improve the detection rate of outliers in monitoring data,on the basis of using the IF algorithm for preliminary identification of outliers,the outlier scores after quantitative calculation were introduced into the cloud model (CM) reverse cloud generator as variables.Based on the cloud digital eigenvalues obtained by the reverse cloud transform,the boundary data was located.The local anomaly factor (LOF) algorithm was further introduced to make the secondary accurate diagnosis of the located boundary data.The surface displacement monitoring data of a tailings dam was taken as an example to verify the model.The results show that for the real outliers and boundary random errors in the monitoring data,the detection rates of the IF model are 16.5% and 22.2%,while the detection rates of the IF-CM-LOF model are 90% and 61.1%,respectively.The diagnostic performance of outliers is obviously better than that of the IF model.

Key words: tailings dam,outlier,monitoring data,detection rate,IF-CM-LOF