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Machine learning improves contamination monitoring

Because groundwater is susceptible to pollution from automotive fuel, fertilizer or naturally occurring substances like iron, the Environmental Protection Agency and its state-level counterparts conduct annual or quarterly sampling and analysis.

However, new research from Lawrence Berkeley National Laboratory shows that sensors and machine learning techniques can provide regular, even continual, monitoring.

At the Savannah River Site, a former nuclear weapons production facility, researcher Haruko Wainwright used in situ sensors to test for radioactivity by measuring groundwater levels of acidity and conductance, which proved be reliable indicators for tritium and uranium-238 concentrations. Sensor data was fed into an algorithm that used the acidity and conductance data to estimate radioactivity. When the results were compared to the historical record, they proved to be a “reliable” source of contamination data.

Using machine learning to quickly analyze sensor data can provide an early warning system  to changes in contaminant levels, Wainwright said in a statement.

About the Author

Matt Leonard is a former reporter for GCN.

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