Identifying Outliers of Non-Gaussian Groundwater State Data Based on Ensemble Estimation for Long-Term Trends

Three modified outlier identification methods: the three sigma rule (3s), inter quantile range (IQR) and median absolute deviation (MAD), which take advantage of the ensemble regression method are proposed. For validation purposes, the performance of the methods is compared using simulated and actual groundwater data with a few hypothetical conditions. In the validations using simulated data, all of the proposed methods reasonably identify outliers at a 5% outlier level; whereas, only the IQR method performs well for identifying outliers at a 30% outlier level. When applying the methods to real groundwater data, the outlier identification performance of the IQR method is found to be superior to the other two methods. However, the IQR method is found to have a limitation in the false identification of excessive outliers, which may be supplemented by joint applications with the other methods (i.e., the 3s rule and MAD methods). The proposed methods can be also applied as a potential tool for future anomaly detection by model training based on currently available data.