WH Modeling Input and output data
The data are comprised of input and output data from Machine Learning models that were developed to predict watershed health (WH) values in HUC-10 sub-watersheds within three major Midwest river basins. The input data included timeseries of hydro-meteorological and reconstructed WQ parameters (sediment, nitrogen, and phosphorus) as well as GIS shape files of watershed attributes (soil, landcover/land use, geomorphology, drainage classes, fertilizer sale data, etc. ). The output data is ensemble-model estimated annual WH values in HUC-10 sub-watersheds within the three river basins. The ensemble-model predicted WH values are derived from WH values obtained from three trained and validated machine learning models.
This dataset is associated with the following publication:
Mallya, G., M.M. Hantush, and R.S. Govindaraju. A Machine Learning Approach to Predict Watershed Health Indices for Sediments and Nutrients at Ungauged Basins. WATER. MDPI, Basel, SWITZERLAND, 15(3): 586, (2023).
Resources
1 resource available
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ezD4762735_Data.xlsx
APPLICATION/VND.OPENXMLFORMATS-OFFICEDOCUMENT.SPREADSHEETML.SHEET
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Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[ "020:00" ] |
| contactPoint |
{ "fn": "Mohamed Hantush", "hasEmail": "mailto:hantush.mohamed@epa.gov" } |
| description | The data are comprised of input and output data from Machine Learning models that were developed to predict watershed health (WH) values in HUC-10 sub-watersheds within three major Midwest river basins. The input data included timeseries of hydro-meteorological and reconstructed WQ parameters (sediment, nitrogen, and phosphorus) as well as GIS shape files of watershed attributes (soil, landcover/land use, geomorphology, drainage classes, fertilizer sale data, etc. ). The output data is ensemble-model estimated annual WH values in HUC-10 sub-watersheds within the three river basins. The ensemble-model predicted WH values are derived from WH values obtained from three trained and validated machine learning models. This dataset is associated with the following publication: Mallya, G., M.M. Hantush, and R.S. Govindaraju. A Machine Learning Approach to Predict Watershed Health Indices for Sediments and Nutrients at Ungauged Basins. WATER. MDPI, Basel, SWITZERLAND, 15(3): 586, (2023). |
| distribution |
[ { "title": "ezD4762735_Data.xlsx", "mediaType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", "downloadURL": "https://pasteur.epa.gov/uploads/10.23719/1528457/ezD4762735_Data.xlsx" } ] |
| identifier | https://doi.org/10.23719/1528457 |
| keyword |
[ "Nitrogen and Co-pollutants", "Phosphorus and Nitrogen", "Watershed Health", "suspended sediment" ] |
| license | https://pasteur.epa.gov/license/sciencehub-license.html |
| modified | 2019-06-14 |
| 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/10.3390/w15030586" ] |
| rights |
null
|
| title | WH Modeling Input and output data |
