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Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data: 5 Model predictions
<p>This data release component contains water temperature predictions in 118 river catchments across the U.S. Predictions are from the four models described by Rahmani et al. (2020): locally-fitted linear regression, LSTM-noQ, LSTM-obsQ, and LSTM-simQ.</p>
Complete Metadata
| accessLevel | public |
|---|---|
| bureauCode |
[
"010:12"
]
|
| contactPoint |
{
"fn": "Farshid Rahmani",
"@type": "vcard:Contact",
"hasEmail": "mailto:fzr5082@psu.edu"
}
|
| description | <p>This data release component contains water temperature predictions in 118 river catchments across the U.S. Predictions are from the four models described by Rahmani et al. (2020): locally-fitted linear regression, LSTM-noQ, LSTM-obsQ, and LSTM-simQ.</p> |
| distribution |
[
{
"@type": "dcat:Distribution",
"title": "Digital Data",
"format": "XML",
"accessURL": "https://doi.org/10.5066/P97CGHZH",
"mediaType": "application/http",
"description": "Landing page for access to the data"
},
{
"@type": "dcat:Distribution",
"title": "Original Metadata",
"format": "XML",
"mediaType": "text/xml",
"description": "The metadata original format",
"downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.5f9865e5d34e198cb77ff08a.xml"
}
]
|
| identifier | http://datainventory.doi.gov/id/dataset/USGS_5f9865e5d34e198cb77ff08a |
| keyword |
[
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"Alabama",
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"Iowa",
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"RI",
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"Wyoming",
"deep learning",
"environment",
"inlandWaters",
"machine learning",
"modeling",
"streams",
"water resources",
"water temperature"
]
|
| modified | 2020-12-09T00:00:00Z |
| publisher |
{
"name": "U.S. Geological Survey",
"@type": "org:Organization"
}
|
| spatial | -123.32988684, 30.1454932, -70.97964444, 48.90595739 |
| theme |
[
"Geospatial"
]
|
| title | Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data: 5 Model predictions |
