@prefix dcat: <http://www.w3.org/ns/dcat#> .
@prefix dct: <http://purl.org/dc/terms/> .
@prefix foaf: <http://xmlns.com/foaf/0.1/> .
@prefix gsp: <http://www.opengis.net/ont/geosparql#> .
@prefix locn: <http://www.w3.org/ns/locn#> .
@prefix vcard: <http://www.w3.org/2006/vcard/ns#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .

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    dct:description "This dataset includes evaluation data (\"test\" data) and performance metrics for water temperature predictions from multiple modeling frameworks. 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. Performance was measured as root-mean squared errors relative to temperature observations during the test period. Test data include compiled water temperature data from a variety of sources, including the Water Quality Portal (Read et al. 2017), the North Temperate Lakes Long-TERM Ecological Research Program (https://lter.limnology.wisc.edu/), the Minnesota department of Natural Resources, and the Global Lake Ecological Observatory Network (gleon.org). This dataset is part of a larger data release of lake temperature model inputs and outputs for 68 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9AQPIVD)." ;
    dct:identifier "cb79e8ca-6b15-42a0-bb27-8c9584161404" ;
    dct:issued "2025-11-20T08:00:33.711244"^^xsd:dateTime ;
    dct:modified "2020-08-20"^^xsd:date ;
    dct:publisher <https://data.amerigeoss.org/organization/727dbdd5-3f98-4ac0-9d28-5e344558139b> ;
    dct:spatial [ a dct:Location ;
            locn:geometry "POLYGON ((-94.2609 42.5692, -87.9475 42.5692, -87.9475 48.6428, -94.2609 48.6428, -94.2609 42.5692))"^^gsp:wktLiteral ] ;
    dct:title "Process-guided deep learning water temperature predictions: 6c All lakes historical evaluation data" ;
    dcat:contactPoint [ a vcard:Organization ;
            vcard:fn "U.S. Geological Survey (Point of Contact)" ;
            vcard:hasEmail <mailto:jread@usgs.gov> ] ;
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    dcat:keyword "amerigeo",
        "amerigeoss",
        "ckan",
        "climate-change",
        "deep-learning",
        "geo",
        "geoss",
        "hybrid-modeling",
        "machine-learning",
        "modeling",
        "national",
        "north-america",
        "reservoirs",
        "temperate-lakes",
        "temperature",
        "thermal-profiles",
        "united-states",
        "us",
        "usgs-5d925048e4b0c4f70d0d0596",
        "water" ;
    dcat:theme <%7Bgeospatial%7D> .

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    dct:title "Web Resource" ;
    dcat:accessURL <https://doi.org/10.5194/gmd-12-473-2019> .

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    dcat:accessURL <http://dx.doi.org/10.1029/2019WR024922> .

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    dct:title "Web Resource" ;
    dcat:accessURL <http://dx.doi.org/10.5066/F7DV1H10> .

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    dct:title "Web Resource" ;
    dcat:accessURL <https://doi.org/10.1002/2016WR019993> .

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