Process-guided deep learning water temperature predictions: 5 Model 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.
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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. |
| distribution |
[ { "@type": "dcat:Distribution", "title": "Digital Data", "format": "XML", "accessURL": "http://dx.doi.org/10.5066/P9AQPIVD", "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.5d915c5de4b0c4f70d0ce51e.xml" } ] |
| identifier | http://datainventory.doi.gov/id/dataset/USGS_5d915c5de4b0c4f70d0ce51e |
| keyword |
[ "007", "012", "MN", "Minnesota", "US", "USGS:5d915c5de4b0c4f70d0ce51e", "United States", "WI", "Wisconsin", "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: 5 Model prediction data |
