Process-guided deep learning water temperature predictions: 5c All lakes historical 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. Zip files for each lake contain four files, one for each of PB, PB0, DL, and PGDL.
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Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[
"010:12"
]
|
| contactPoint |
{
"fn": "Jordan S. Read",
"@type": "vcard:Contact",
"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",
"title": "Digital Data",
"format": "XML",
"accessURL": "http://dx.doi.org/10.5066/P9AQPIVD",
"mediaType": "application/http",
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"title": "Original Metadata",
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"downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.5d915c8ee4b0c4f70d0ce520.xml"
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|
| 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 |
{
"name": "U.S. Geological Survey",
"@type": "org:Organization"
}
|
| 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 |
