Machine learning to predict tributary phosphorus loads data
Description
The water and climate data for Lake Erie, including:
Soil moisture, streamflow, water temperature, evaporation, baseflow.
This dataset is associated with the following publication:
Chang, F., M. Astitha, Y. Yuan, C. Tang, P. Vlahos, V. Cover, and U. Khaira. A New Approach to Predict Tributary Phosphorus Loads Using Machine Learning– and Physics-Based Modeling Systems.. Artificial Intelligence for the Earth Systems. American Meteorological Society, Boston, MA, USA, 2(3): 1-20, (2023).
Resources
| Name |
Format |
Description |
Link |
|
0 |
|
https://doi.org/10.17605/OSF.IO/C4HZ7 |
|
0 |
|
https://edg.epa.gov/data/public/ |
Tags
- nitrogen-and-co-pollutants
- multi-media-modeling
- eutrophication
- nutrient-use
- land-surface-model