Process-guided deep learning water temperature predictions: 5 Model prediction data
Description
Multiple modeling frameworks were used to predict daily temperatures at 0.5m depth intervals for a set of diverse lakes in the U.S. states of Minnesota and Wisconsin. Process-Based (PB) models were configured and calibrated with training data to reduce root-mean squared error. Uncalibrated models used default configurations (PB0; see Winslow et al. 2016 for details) and no parameters were adjusted according to model fit with observations. Deep Learning (DL) models were Long Short-Term Memory artificial recurrent neural network models which used training data to adjust model structure and weights for temperature predictions (Jia et al. 2019). Process-Guided Deep Learning (PGDL) models were DL models with an added physical constraint for energy conservation as a loss term. These models were pre-trained with uncalibrated Process-Based model outputs (PB0) before training on actual temperature observations.
Resources
| Name |
Format |
Description |
Link |
|
55 |
The metadata original format |
https://data.usgs.gov/datacatalog/metadata/USGS.5d915c5de4b0c4f70d0ce51e.xml |
|
55 |
Landing page for access to the data |
http://dx.doi.org/10.5066/P9AQPIVD |
Tags
- environment
- mn
- reservoirs
- hybrid-modeling
- 012
- modeling
- temperate-lakes
- deep-learning
- wi
- united-states
- usgs-5d915c5de4b0c4f70d0ce51e
- thermal-profiles
- 007
- us
- inlandwaters
- climate-change
- water
- temperature
- wisconsin
- minnesota
- machine-learning