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James E Saiers

Biographic Data

ID6840211
NAMEJames E Saiers
GIVEN NAMESJames E
FAMILY NAMESaiers
SIGNATURESAIERS J E
AFFILIATIONSYale University
ORCID0000-0002-0117-3347
VERIFIEDYes
TOTAL WORKS3
TOTAL CITATIONS1
WORKS AS AUTHOR3
WORKS AS EDITOR0
FIRST PUBLICATION YEAR2012
LATEST PUBLICATION YEAR2021
H-INDEX1
  • Assessment of groundwater well vulnerability to contamination through physics-informed machine learning

    Open Access•Mario Soriano, Helen Siegel et al.•ARTICLE•Environmental Research Letters•2021

    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 observatio…

  • Community concern and government response

    Open Access•Cassandra J Clark, Joshua Warren et al.•ARTICLE•Energy Research & Social Science•2021•References: 14

  • The impact of fertilization and hydrology on nitrate fluxes from Mississippi watersheds

    Open Access•Peter A Raymond, Mark B David et al.•ARTICLE•Current Opinion in Environmental…•2012•Cited by: 1•References: 3

  • The impact of fertilization and hydrology on nitrate fluxes from Mississippi watersheds

    Open Access•Peter A Raymond, Mark B David et al.•ARTICLE•Current Opinion in Environmental…•2012•Cited by: 1•References: 3

  • The impact of fertilization and hydrology on nitrate fluxes from Mississippi watersheds

    Open Access•Peter A Raymond, Mark B David et al.•ARTICLE•Current Opinion in Environmental…•2012•Cited by: 1•References: 3

  • Assessment of groundwater well vulnerability to contamination through physics-informed machine learning

    Open Access•Mario Soriano, Helen Siegel et al.•ARTICLE•Environmental Research Letters•2021

    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 observatio…

  • Community concern and government response

    Open Access•Cassandra J Clark, Joshua Warren et al.•ARTICLE•Energy Research & Social Science•2021•References: 14

Environmental Science (3 works) · Atmospheric and Environmental Gas Dynamics (2 works) · Geology (2 works) · Hydrology (agriculture (2 works) · Agriculture (1 work) · Agronomy (1 work) · Aquifer (1 work) · Business (1 work) · Computer Science (1 work) · Contamination (1 work)

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