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).

Data and Resources

Field Value
accessLevel public
bureauCode {020:00}
catalog_conformsTo https://project-open-data.cio.gov/v1.1/schema
identifier https://doi.org/10.23719/1528457
license https://pasteur.epa.gov/license/sciencehub-license.html
modified 2019-06-14
programCode {020:000}
publisher U.S. EPA Office of Research and Development (ORD)
publisher_hierarchy U.S. Government > U.S. Environmental Protection Agency > U.S. EPA Office of Research and Development (ORD)
references {https://doi.org/10.3390/w15030586}
resource-type Dataset
source_datajson_identifier true
source_hash e8e4bb7217746c4d1360205f4d98e055ef112eef
source_schema_version 1.1
Groups
  • AmeriGEOSS
  • National Provider
  • North America
Tags
  • AmeriGEO
  • AmeriGEOSS
  • CKAN
  • GEO
  • GEOSS
  • National
  • North America
  • United States
  • nitrogen-and-co-pollutants
  • phosphorus-and-nitrogen
  • suspended-sediment
  • watershed-health
isopen False
license_id other-license-specified
license_title other-license-specified
maintainer Mohamed Hantush
maintainer_email hantush.mohamed@epa.gov
metadata_created 2025-09-24T02:50:14.561971
metadata_modified 2025-09-24T02:50:14.561981
notes 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).
num_resources 1
num_tags 12
title WH Modeling Input and output data