{"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": ["biota", "inlandWaters", "United States", "environment", "US", "MN", "reservoirs", "deep learning", "Minnesota", "machine learning", "temperature", "USGS:5d915c8ee4b0c4f70d0ce520", "modeling", "climate change", "Wisconsin", "WI", "hybrid modeling", "thermal profiles", "water", "temperate lakes"], "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"}