{"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. \n\nThis dataset is associated with the following publication:\nFeng 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\u2013 and Physics-Based Modeling Systems.    Artificial Intelligence for the Earth Systems. American Meteorological Society, Boston, MA, USA, 2(3): 1-43, (2023).", "distribution": [{"downloadURL": "https://pasteur.epa.gov/uploads/10.23719/1529548/FengChang%20et%20al_ML%20Outputs.xlsx", "mediaType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", "title": "FengChang et al_ML Outputs.xlsx"}], "identifier": "https://doi.org/10.23719/1529548", "keyword": ["tributary phosphorus loads", "machine learning", "eutrophication", "numerical prediction models"], "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"}