Linking multi-media modeling with machine learning to assess and predict lake chlorophyll a concentrations
The datasets include hydrological parameters such as streamflow, soil moisture and water temperature, and meteorological data such as precipitation, max and min temperature, evaporation from 2002 to 2017 for Lake Erie.
This dataset is associated with the following publication:
Feng Chang, C., V. Cover, C. Tang, P. Vlahos, D. Wanik, J. Yan, J. Bash, and M. Astitha. Linking multi-media modeling with machine learning to assess and predict lake chlorophyll a concentrations. JOURNAL OF GREAT LAKES RESEARCH. International Association for Great Lakes Research, Ann Arbor, MI, USA, 47(6): 1656-1670, (2021).
Complete Metadata
| accessLevel | public |
|---|---|
| bureauCode |
[
"020:00"
]
|
| contactPoint |
{
"fn": "Chunling Tang",
"hasEmail": "mailto:tang.chunling@epa.gov"
}
|
| description | The datasets include hydrological parameters such as streamflow, soil moisture and water temperature, and meteorological data such as precipitation, max and min temperature, evaporation from 2002 to 2017 for Lake Erie. This dataset is associated with the following publication: Feng Chang, C., V. Cover, C. Tang, P. Vlahos, D. Wanik, J. Yan, J. Bash, and M. Astitha. Linking multi-media modeling with machine learning to assess and predict lake chlorophyll a concentrations. JOURNAL OF GREAT LAKES RESEARCH. International Association for Great Lakes Research, Ann Arbor, MI, USA, 47(6): 1656-1670, (2021). |
| distribution |
[
{
"title": "VICdata-2002-2017.xlsx",
"mediaType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
"downloadURL": "https://pasteur.epa.gov/uploads/10.23719/1524526/VICdata-2002-2017.xlsx"
}
]
|
| identifier | https://doi.org/10.23719/1524526 |
| keyword |
[
": Precipitation",
"air temperatire",
"soil moisture",
"streamflow",
"water temperature"
]
|
| license | https://pasteur.epa.gov/license/sciencehub-license.html |
| modified | 2022-04-08 |
| 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/10.1016/j.jglr.2021.09.011",
"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9364922"
]
|
| rights |
null
|
| title | Linking multi-media modeling with machine learning to assess and predict lake chlorophyll a concentrations |
