Event Detection in Groundwater Sensor Networks using Artificial Intelligence

In this paper, a method for detecting spatial and temporal anomalous events in groundwater sensor networks (high-dimensional time series data), such as system faults and attacks will be developed. Unlike recently developed deep learning frameworks for anomaly detection which do not consider the dependences between the variables and apply the existing relationships to predict the expected behavior of the sensors, the method in this paper extracts the relationships between the sensors spatially and temporally and learn to detect and simultaneously explain deviations from these relationships. This challenge is solved by using graph attention neural networks and structured learning. Attention neural networks can give useful interpretability in context of the anomalies detected and allows to identify their causes. To improve robustness the method considers aleatoric and parametric uncertainties by using ensemble specific value prediction and prediction intervals without assuming any data distribution. Furthermore, the model was connected to a fully connected classifier to classify typical groundwater network anomalies. The method was applied to a study area and it could be shown that the method could capture in 92% of the cases the complex correlations between the high dimensional variables, and enabled analysts to identify the causes of the anomalies.