Process-guided deep learning water temperature predictions: 5 Model prediction data
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.
Complete Metadata
| bureauCode |
[ "010:12" ] |
|---|---|
| identifier | http://datainventory.doi.gov/id/dataset/USGS_5d915c5de4b0c4f70d0ce51e |
| spatial | -94.2609062307949, 42.5692312672573, -87.9475441739278, 48.6427837911633 |
| theme |
[ "Geospatial" ] |
