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    • FengChang et al_ML Output.xlsx

    Metadata Updated: September 16, 2023

    Outputs from WRF, EPIC, VIC. Outputs and analysis from the ML-based model described in the paper.

    This dataset is associated with the following publication: Feng Chang, C., M. Astitha, Y. Yuan, C. Tang, P. Vlahos, V. Garcia, and U. Khaira. A New Approach to Predict Tributary Phosphorus Loads Using Machine Learning– and Physics-Based Modeling Systems. Artificial Intelligence for the Earth Systems. American Meteorological Society, Boston, MA, USA, 2(3): 1-43, (2023).

    Access & Use Information

    Public: This dataset is intended for public access and use. License: See this page for license information.

    Downloads & Resources

    References

    https://doi.org/10.1175/aies-d-22-0049.1

    Dates

    Metadata Created Date September 16, 2023
    Metadata Updated Date September 16, 2023

    Metadata Source

    Harvested from EPA ScienceHub

    Other Data Resources

    Additional Metadata

    Resource Type Dataset
    Metadata Created Date September 16, 2023
    Metadata Updated Date September 16, 2023
    Publisher U.S. EPA Office of Research and Development (ORD)
    Maintainer
    Identifier https://doi.org/10.23719/1529548
    Data Last Modified 2023-07-06
    Public Access Level public
    Bureau Code 020:00
    Schema Version https://project-open-data.cio.gov/v1.1/schema
    Harvest Object Id 0e5565de-2d7f-45e4-8ec7-e735b304b6e8
    Harvest Source Id 04b59eaf-ae53-4066-93db-80f2ed0df446
    Harvest Source Title EPA ScienceHub
    License https://pasteur.epa.gov/license/sciencehub-license-non-epa-generated.html
    Program Code 020:000
    Publisher Hierarchy U.S. Government > U.S. Environmental Protection Agency > U.S. EPA Office of Research and Development (ORD)
    Related Documents https://doi.org/10.1175/aies-d-22-0049.1
    Source Datajson Identifier True
    Source Hash e41fa53dd88886d4631cd89038c20b5790778022fd2f779c7bab56d9f7a50c9e
    Source Schema Version 1.1

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