Process-guided deep learning water temperature predictions: 4c All lakes historical training data
Description
Observed water temperatures from 1980-2018 were compiled for 68 lakes in Minnesota and Wisconsin (USA). These data were used as training data for process-guided deep learning models and deep learning models, and calibration data for process-based models. The data are formatted as a single csv (comma separated values) file with attributes corresponding to the unique combination of lake identifier, time, and depth. Data came from a variety of sources, including the Water Quality Portal, the North Temperate Lakes Long-Term Ecological Research Project, and digitized temperature records from the MN Department of Natural Resources.
Resources
| Name |
Format |
Description |
Link |
|
55 |
The metadata original format |
https://data.usgs.gov/datacatalog/metadata/USGS.5d8a47bce4b0c4f70d0ae61f.xml |
|
55 |
Landing page for access to the data |
http://dx.doi.org/10.5066/P9AQPIVD |
Tags
- environment
- mn
- reservoirs
- hybrid-modeling
- usgs-5d8a47bce4b0c4f70d0ae61f
- modeling
- biota
- temperate-lakes
- deep-learning
- wi
- united-states
- thermal-profiles
- us
- inlandwaters
- climate-change
- water
- temperature
- wisconsin
- minnesota
- machine-learning