{
  "accessLevel": "public",
  "bureauCode": [
    "010:12"
  ],
  "contactPoint": {
    "@type": "vcard:Contact",
    "fn": "Jordan S. Read",
    "hasEmail": "mailto:jread@usgs.gov"
  },
  "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. Zip files for each lake contain four files, one for each of PB, PB0, DL, and PGDL.",
  "distribution": [
    {
      "@type": "dcat:Distribution",
      "accessURL": "http://dx.doi.org/10.5066/P9AQPIVD",
      "description": "Landing page for access to the data",
      "format": "XML",
      "mediaType": "application/http",
      "title": "Digital Data"
    },
    {
      "@type": "dcat:Distribution",
      "description": "The metadata original format",
      "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.5d915c8ee4b0c4f70d0ce520.xml",
      "format": "XML",
      "mediaType": "text/xml",
      "title": "Original Metadata"
    }
  ],
  "identifier": "http://datainventory.doi.gov/id/dataset/USGS_5d915c8ee4b0c4f70d0ce520",
  "keyword": [
    "MN",
    "Minnesota",
    "US",
    "USGS:5d915c8ee4b0c4f70d0ce520",
    "United States",
    "WI",
    "Wisconsin",
    "biota",
    "climate change",
    "deep learning",
    "environment",
    "hybrid modeling",
    "inlandWaters",
    "machine learning",
    "modeling",
    "reservoirs",
    "temperate lakes",
    "temperature",
    "thermal profiles",
    "water"
  ],
  "modified": "2020-08-20T00:00:00Z",
  "publisher": {
    "@type": "org:Organization",
    "name": "U.S. Geological Survey"
  },
  "spatial": "-94.2609062307949, 42.5692312672573, -87.9475441739278, 48.6427837911633",
  "theme": [
    "geospatial"
  ],
  "title": "Process-guided deep learning water temperature predictions: 5c All lakes historical prediction data"
}