Reliable groundwater monitoring depends on distinguishing genuine environmental changes from measurement errors and sensor drift. For this project, tailor-made statistical algorithms were developed to detect anomalies in air-pressure and hydrostatic-pressure time series.

The algorithms were documented and implemented in gwloggeR, a publicly available R package developed for INBO and shared on github. Beyond automated detection, diagnostic plots help users interpret the results by highlighting outliers, structural changes, temporal effects and potential sensor drift.

The project combines statistical modelling with practical software development, turning specialized detection methods into workflows for environmental data validation.