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Stream Temperature Machine Learning Review Performance Metrics Data

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Type:	Resource
Storage:	The size of this resource is 224.3 KB
Created:	Aug 07, 2024 at 7:08 p.m.
Last updated:	Aug 15, 2024 at 11:09 p.m.
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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 3 and 4. 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.

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Coverage

Spatial

Coordinate System/Geographic Projection:

WGS 84 EPSG:4326

Coordinate Units:

Decimal degrees

North Latitude

79.0000°

East Longitude

177.0000°

South Latitude

-45.0000°

West Longitude

-129.0000°

Temporal

Start Date:
End Date:

Content

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Related Resources

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
National Oceanic and Atmospheric Administration	Cooperative Institute for Research to Operations in Hydrology	NA22NWS4320003

How to Cite

Corona, C. R., T. S. Hogue (2024). Stream Temperature Machine Learning Review Performance Metrics Data, HydroShare, http://www.hydroshare.org/resource/ad22cab56ea84b3f99a7b9557c4adfa8

This resource is shared under the Creative Commons Attribution CC BY.

http://creativecommons.org/licenses/by/4.0/

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