Assessment of groundwater well vulnerability to contamination through physics-informed machine learning
Name
Soriano_2021_Environ._Res._Lett._16_084013.pdf
Description
Published version
Size
2.31 MB
Format
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Checksum (MD5)
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Author(s)
Soriano, Mario A
Siegel, Helen G
Johnson, Nicholaus P
Gutchess, Kristina M
Xiong, Boya
Li, Yunpo
Clark, Cassandra J
Plata, Desiree L
Deziel, Nicole C
Saiers, James E
Date Issued
July 2021
Journal
Environmental Research Letters
Publisher
IOP Publishing
Citation
Mario A Soriano Jr et al 2021 Environ. Res. Lett. 16 084013
Version
Final published version
Abstract
Contamination from anthropogenic activities is a long-standing challenge to the sustainability of
groundwater resources. Physically based (PB) models are often used in groundwater risk
assessments, but their application to large scale problems requiring high spatial resolution remains
computationally intractable. Machine learning (ML) models have emerged as an alternative to PB
models in the era of big data, but the necessary number of observations may be impractical to
obtain when events are rare, such as episodic groundwater contamination incidents. The current
study employs metamodeling, a hybrid approach that combines the strengths of PB and ML
models while addressing their respective limitations, to evaluate groundwater well vulnerability to
contamination from unconventional oil and gas development (UD). We illustrate the approach in
northeastern Pennsylvania, where intensive natural gas production from the Marcellus Shale
overlaps with local community dependence on shallow aquifers. Metamodels were trained to
classify vulnerability from predictors readily computable in a geographic information system. The
trained metamodels exhibited high accuracy (average out-of-bag classification error <5%). A
predictor combining information on topography, hydrology, and proximity to contaminant
sources (inverse distance to nearest upgradient UD source) was found to be highly important for
accurate metamodel predictions. Alongside violation reports and historical groundwater quality
records, the predicted vulnerability provided critical insights for establishing the prevalence of UD
contamination in 94 household wells that we sampled in 2018. While <10% of the sampled wells
exhibited chemical signatures consistent with UD produced wastewaters, >60% were predicted to
be in vulnerable locations, suggesting that future impacts are likely to occur with greater frequency
if safeguards against contaminant releases are relaxed. Our results show that hybrid
physics-informed ML offers a robust and scalable framework for assessing groundwater
contamination risks.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
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Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1088/1748-9326/ac10e0
