Machine Learning Creates More Complete Picture of Groundwater Contamination

Machine learning algorithms can fill gaps in sparse or incomplete groundwater datasets, according to researchers partially funded by the NIEHS Superfund Research Program. The study tested the ability of two algorithms to help scientists analyze co-occurring pollutants in groundwater by filling in missing field data points and was led by researchers from Arizona State University, Harvard University, and North Carolina State University.