Toward automating post processing of aquatic sensor data

Sensors measuring environmental phenomena at high frequency commonly report anomalies related to fouling, sensor drift and calibration, and datalogging and transmission issues. Suitability of data for analyses and decision making often depends on manual review and adjustment of data. Machine learning techniques have potential to automate identification and correction of anomalies, streamlining the quality control process. We explored approaches for automating anomaly detection and correction of aquatic sensor data for implementation in a Python package (pyhydroqc). We applied both classical and deep learning time series regression models that estimate values, identify anomalies based on dynamic thresholds, and offer correction estimates. Techniques were developed and performance assessed using data reviewed, corrected, and labeled by technicians in an aquatic monitoring use case. Auto-Regressive Integrated Moving Average (ARIMA) consistently performed best, and aggregating results from multiple models improved detection. pyhydroqc includes custom functions and a workflow for anomaly detection and correction.