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Outliers due to technical errors in water-quality data from in situ sensorscan reduce data quality and have a direct impact on inference drawn fromsubsequent data analysis. However, outlier detection through manual monitoringis unfeasible given the volume and velocity of data the sensors produce. Here,we proposed an automated framework that provides early detection of outliers inwater-quality data from in situ sensors caused by technical issues.Theframework was used first to identify the data features that differentiateoutlying instances from typical behaviours. Then statistical transformationswere applied to make the outlying instances stand out in transformed dataspace. Unsupervised outlier scoring techniques were then applied to thetransformed data space and an approach based on extreme value theory was usedto calculate a threshold for each potential outlier. Using two data setsobtained from in situ sensors in rivers flowing into the Great Barrier Reeflagoon, Australia, we showed that the proposed framework successfullyidentified outliers involving abrupt changes in turbidity, conductivity andriver level, including sudden spikes, sudden isolated drops and level shifts,while maintaining very low false detection rates. We implemented this frameworkin the open source R package oddwater.

A feature-based framework for detecting technical outliers in water-quality data from in situ sensors