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    • 4 Model Code: Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins

    Metadata Updated: January 8, 2026

    <p>This data release component contains model code and configurations for the LSTM models used to predict stream temperature.</p>

    Access & Use Information

    Public: This dataset is intended for public access and use. License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

    Downloads & Resources

    Dates

    Metadata Created Date January 8, 2026
    Metadata Updated Date January 8, 2026

    Metadata Source

    Harvested from DOI USGS DCAT-US

    Other Data Resources

    Additional Metadata

    Resource Type Dataset
    Metadata Created Date January 8, 2026
    Metadata Updated Date January 8, 2026
    Publisher U.S. Geological Survey
    Maintainer
    Identifier http://datainventory.doi.gov/id/dataset/USGS_6084cb16d34eadd49d31aead
    Data Last Modified 2021-09-27T00:00:00Z
    Category geospatial
    Public Access Level public
    Bureau Code 010:12
    Metadata Context https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld
    Metadata Catalog ID https://ddi.doi.gov/usgs-data.json
    Schema Version https://project-open-data.cio.gov/v1.1/schema
    Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
    Harvest Object Id 5e15cb3c-3b3c-4831-b4fc-32e8c1f19c99
    Harvest Source Id 2b80d118-ab3a-48ba-bd93-996bbacefac2
    Harvest Source Title DOI USGS DCAT-US
    Metadata Type geospatial
    Old Spatial -124.138658984335, 29.1524975232233, -67.8714112090545, 49.0018341836332
    Source Datajson Identifier True
    Source Hash 3a17333e901dcb5042569228563c4d277379905509822aa862569d98e73d4c06
    Source Schema Version 1.1
    Spatial {"type": "Polygon", "coordinates": -124.138658984335, 29.1524975232233, -124.138658984335, 49.0018341836332, -67.8714112090545, 49.0018341836332, -67.8714112090545, 29.1524975232233, -124.138658984335, 29.1524975232233}

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