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FengChang et al_ML Output.xlsx
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).
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1 resource available
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FengChang et al_ML Outputs.xlsx
APPLICATION/VND.OPENXMLFORMATS-OFFICEDOCUMENT.SPREADSHEETML.SHEET
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Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[ "020:00" ] |
| contactPoint |
{ "fn": "Yongping Yuan", "hasEmail": "mailto:yuan.yongping@epa.gov" } |
| description | 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). |
| distribution |
[ { "title": "FengChang et al_ML Outputs.xlsx", "mediaType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", "downloadURL": "https://pasteur.epa.gov/uploads/10.23719/1529548/FengChang%20et%20al_ML%20Outputs.xlsx" } ] |
| identifier | https://doi.org/10.23719/1529548 |
| keyword |
[ "eutrophication", "machine learning", "numerical prediction models", "tributary phosphorus loads" ] |
| license | https://pasteur.epa.gov/license/sciencehub-license-non-epa-generated.html |
| modified | 2023-07-06 |
| programCode |
[ "020:000" ] |
| publisher |
{ "name": "U.S. EPA Office of Research and Development (ORD)", "subOrganizationOf": { "name": "U.S. Environmental Protection Agency", "subOrganizationOf": { "name": "U.S. Government" } } } |
| references |
[ "https://doi.org/10.1175/aies-d-22-0049.1" ] |
| rights |
null
|
| title | FengChang et al_ML Output.xlsx |
