Soriano, Mario A Jr https://orcid.org/0000-0003-0499-9352
Siegel, Helen G https://orcid.org/0000-0002-3412-5851
Johnson, Nicholaus P https://orcid.org/0000-0003-1346-3576
Gutchess, Kristina M https://orcid.org/0000-0002-9745-5049
Xiong, Boya https://orcid.org/0000-0002-7994-3508
Li, Yunpo https://orcid.org/0000-0002-1172-2741
Clark, Cassandra J https://orcid.org/0000-0002-5993-4540
Plata, Desiree L https://orcid.org/0000-0003-0657-7735
Deziel, Nicole C https://orcid.org/0000-0002-5751-9191
Saiers, James E https://orcid.org/0000-0002-0117-3347
Funding for this research was provided by:
U.S. Environmental Protection Agency (CR839249)
Institute for Biospheric Studies, Yale University (Small Grants Program)
Geological Society of America (Graduate Student Research Grants)
National Institute of Environmental Health Sciences (F31ES031441)
Article Title: Assessment of groundwater well vulnerability to contamination through physics-informed machine learning
Journal Title: Environmental Research Letters
Article Type: paper
Copyright Information: © 2021 The Author(s). Published by IOP Publishing Ltd
Publication dates
Date Received: 2021-03-05
Date Accepted: 2021-07-02
Online publication date: 2021-07-22