Margaux Jeanne Sleckman

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Predictive understanding of stream salinization in a developed watershed using machine learning

Oct 16, 2024 | pubs.usgs.gov | Jared Smith |Lauren Koenig |Margaux Jeanne Sleckman |Alison P. Appling

Stream salinization is a global issue, yet few models can provide reliable salinity estimates for unmonitored locations at the time scales required for ecological exposure assessments. Machine learning approaches are presented that use spatially limited high-frequency monitoring and spatially distributed discrete samples to estimate the daily stream-specific conductance across a watershed.
Train, inform, borrow, or combine? Approaches to process-guided deep learning for groundwater-influenced stream temperature prediction

Jan 11, 2024 | pubs.usgs.gov | Janet Barclay |Simon Topp |Lauren Koenig |Margaux Jeanne Sleckman

Links More information: Publisher Index Page (via DOI) Open Access Version: Publisher Index Page Download citation as: RIS | Dublin Core Study Area Publication type Article Publication Subtype Journal Article Title Train, inform, borrow, or combine?

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