{"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. \n\nThis dataset is associated with the following publication:\nMallya, 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": [{"downloadURL": "https://pasteur.epa.gov/uploads/10.23719/1528457/ezD4762735_Data.xlsx", "mediaType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", "title": "ezD4762735_Data.xlsx"}], "identifier": "https://doi.org/10.23719/1528457", "keyword": ["suspended sediment", "Nitrogen and Co-pollutants", "Phosphorus and Nitrogen", "Watershed Health"], "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  "}