Artificial Intelligence for EM Problem Set (Soil and Groundwater) – Machine Learning Modeling to Identify Temporal and Spatial Relationships between Inland and Shoreline Hexavalent Chromium [Cr(VI)] Concentrations in 100 Areas

Hexavalent chromium (Cr(VI)) is one of the primary contaminants in the 100 Areas at the U.S. Department of Energy’s (DOE’s) Hanford Site. Various cleanup efforts are ongoing to remediate this waste site since the late 1990s. To estimate the effects of these cleanup efforts and plan future cleanup actions, it is necessary to analyze Cr(VI) dynamics in the groundwater and surface water. The monitoring data available for groundwater wells and aquifer tubes, as well as water table levels and river stage monitoring sampled at 100 Areas, can be used with Artificial Intelligence/Machine Learning (AI/ML) algorithms to understand complex hydrogeological processes and interactions among the aquifers and the dynamic river stage in the Columbia River. AI/ML algorithms can leverage high-performance computing to predict the spatial and temporal distribution of Cr(VI) and also help in identifying any spatiotemporal relationship in the dataset.