ALTEMIS: Next-Generation in situ Real-time Groundwater Monitoring Strategies

Abstract: The Advanced Long-Term Monitoring Systems (ALTEMIS) project aims to establish the new paradigm of long-term monitoring based on state-of-art technologies – in situ groundwater sensors, geophysics, drone/satellite-based remote sensing, reactive transport modeling, and AI – that will improve effectiveness and robustness, while reducing the overall cost. As a part of this project, this study aims to develop an in situ real-time groundwater long-term monitoring (LTM) framework based on various sensors and data analytics methods. Rather than relying on one single metric, our approach provides multiple lines of evidence to ensure the system stability: (1) groundwater table and its gradient that governs the migration speed and direction of the contaminant plume, and (2) in situ measurable geochemical parameters (specific conductance, pH, and others) for detecting changes in contaminant mobility. In addition, we develop machine learning algorithms to (a) improve the spatiotemporal interpolation of groundwater tables and contaminant concentrations by exploiting proxy variables such as in situ sensors and geospatial layers, and (b) detect anomalies by computing the difference between near-future forecasting and measurements. To accommodate noisy and drifting sensor data streams, we also developed algorithms for automated outliers removal and drift correction. We demonstrate the framework based on the plot-scale installation at the Savannah River Site F-Area in the US.