WH Modeling Input and output data
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).
Resources
| Name |
Format |
Description |
Link |
|
53 |
ezD4762735_Data.xlsx |
https://pasteur.epa.gov/uploads/10.23719/1528457/ezD4762735_Data.xlsx |
Tags
- nitrogen-and-co-pollutants
- suspended-sediment
- phosphorus-and-nitrogen
- watershed-health