A Feature-Based Framework for Detecting Technical Outliers in Water-Quality Data from In Situ Sensors

Outliersdue to technical errors in water-quality data from in situ sensors can reduce data quality and have a direct impact on inference drawn from subsequent data analysis. However, outlierdetection through manual monitoring is unfeasible given the volume and velocity of data the sensors produce. Here, we proposed an automated framework that provides early detection of outliersin water-quality data from in situ sensors caused by technical issues.The framework was used first to identify the data features that differentiate outlying instances from typical behaviours. Then statistical transformations were applied to make the outlying instances stand out in transformed data space. Unsupervised outlierscoring techniques were then applied to the transformed data space and an approach based on extreme value theorywas used to calculate a threshold for each potential outlier. Using two data sets obtained from in situ sensors in rivers flowing into the Great Barrier Reef lagoon, Australia, we showed that the proposed framework successfully identified outliersinvolving abrupt changes in turbidity, conductivity and river level, including sudden spikes, sudden isolated drops and level shifts, while maintaining very low false detection rates. We implemented this framework in the open source R package oddwater.