Process-guided deep learning water temperature predictions: 6 Model evaluation (test data and RMSE)

Description

This dataset includes evaluation data ("test" data) and performance metrics for water temperature predictions from multiple modeling frameworks. 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. Performance was measured as root-mean squared errors relative to temperature observations during the test period. Test data include compiled water temperature data from a variety of sources, including the Water Quality Portal (Read et al. 2017), the North Temperate Lakes Long-TERM Ecological Research Program (https://lter.limnology.wisc.edu/), the Minnesota department of Natural Resources, and the Global Lake Ecological Observatory Network (gleon.org). This dataset is part of a larger data release of lake temperature model inputs and outputs for 68 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9AQPIVD).

Resources

Name Format Description Link
55 Landing page for access to the data http://dx.doi.org/10.5066/P9AQPIVD
55 The metadata original format https://data.usgs.gov/datacatalog/metadata/USGS.5d925023e4b0c4f70d0d0594.xml

Tags

  • environment
  • mn
  • reservoirs
  • hybrid-modeling
  • 012
  • modeling
  • temperate-lakes
  • deep-learning
  • wi
  • united-states
  • thermal-profiles
  • 007
  • us
  • inlandwaters
  • usgs-5d925023e4b0c4f70d0d0594
  • climate-change
  • water
  • temperature
  • wisconsin
  • minnesota
  • machine-learning

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