Statistical Outlier Removal in High-Frequency River Water-Quality Data: Effects on Total Phosphorus Virtual Sensing and on the Retention of Rainfall-Driven Events

Statistical outlier detection is routine preprocessing for high-frequency water-quality records, but extreme values are of two kinds: technical anomalies and the response to short-term hydrological events. This study asks how that choice affects a total phosphorus (TP) virtual sensor and the information retained. The interquartile range (IQR), Z-score, robust Mahalanobis distance and Isolation Forest were compared with a baseline without statistical outlier removal on 17,059 hourly observations from a Hoeincheon watershed station, Republic of Korea, with thresholds fitted on the training period only and Random Forest and Gradient Boosting scored on a common test set. Removal rates ranged from 0.93% to 20.53%, with no monotonic relation to performance. The Z-score, removing 2.88%, produced the lowest error of the five conditions in this evaluation period for both models; after adjustment for multiple comparisons this reduction was significant for Gradient Boosting only. IQR, removing 20.53%, produced the highest error and removed 98.01% of the rainfall-influenced high-concentration observations. Those observations are both the examples needed to predict elevated TP and the intervals carrying the event structure, so discarding them degraded prediction and fragmented the series together. Preprocessing should therefore be assessed against event retention and temporal continuity as well as error metrics.