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
0 https://doi.org/10.5194/gmd-12-473-2019
0 https://doi.org/10.5066/F7D798MJ
0 CSDGM IMPORT ERROR: No digtinfo/formcont http://dx.doi.org/10.5066/P9AQPIVD
0 http://dx.doi.org/10.5066/F7DV1H10
55 The metadata original source https://data.doi.gov/harvest/object/9d8a2735-5b2a-4bdd-8dc1-adf30d0df59e
0 http://dx.doi.org/10.1029/2019WR024922
33 1810.02880.pdf https://arxiv.org/pdf/1810.02880.pdf

Tags

  • reservoirs
  • hybrid-modeling
  • modeling
  • temperate-lakes
  • deep-learning
  • united-states
  • usgs-5d915c5de4b0c4f70d0ce51e
  • thermal-profiles
  • us
  • climate-change
  • water
  • temperature
  • machine-learning

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