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Created: | Mar 23, 2025 at 11:23 p.m. | |
Last updated: | Mar 23, 2025 at 11:38 p.m. | |
Citation: | See how to cite this resource |
Sharing Status: | Public |
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Abstract
This resource contains performance metric data (R2, RMSE, NSE, MAE) compiled from published literature that used machine learning for stream water temperature modeling. The files in this resource were used to create the scatter plots and box plots for Figures 1 to 4 of the review paper, as well as data used to create Tables 1 and 2. We also include supplementary tables (Supplementary Info PDF file) for sources cited. We recommend the user reads the README file to understand organization of contents.
Finally, we note that these data and related items of information have not been formally disseminated by NOAA, and do not represent any agency determination, view, or policy.
Subject Keywords
Coverage
Spatial
Temporal
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Content
Related Resources
This resource updates and replaces a previous version | Corona, C. R., T. S. Hogue (2025). Stream Temperature Machine Learning Review Performance Metrics Data, HydroShare, http://www.hydroshare.org/resource/face1101c23240d8986599a7f8644db2 |
This resource is referenced by | Corona, C. and Hogue, T.: A Critical Look at Machine Learning Algorithms in River/Stream Water Temperature Modeling , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7003, https://doi.org/10.5194/egusphere-egu24-7003, 2024. |
Credits
Funding Agencies
This resource was created using funding from the following sources:
Agency Name | Award Title | Award Number |
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National Oceanic and Atmospheric Administration | Cooperative Institute for Research to Operations in Hydrology | NA22NWS4320003 |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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