Automated detection of unusual soil moisture probe response patterns with association rule learning

In-situ field monitoring networks generate vast quantities of continuous data can help to improve the design, management, operation and maintenance of Green Infrastructure (GI) systems. However, such actions require efficient and reliable quality assurance quality control (QAQC). In this paper, we develop a rule-based learning algorithm involving Dynamic Time Warping (DTW) to investigate the feasibility of detecting anomalous responses from soil moisture probes using data collected from a GI site in Milwaukee, WI. As an enhancement to traditional QAQC methods which rely on individual time steps, this method converts the continuous time series into event sequences from which response patterns can be detected. Association rules are developed on both environmental features and event features. The results suggest that this method could be used to identify abnormal change patterns as compared to intra-site historical observations. Though developed for soil moisture, this method could easily be extended to apply on other continuous environmental datasets.