@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#> .

<https://data.amerigeoss.org/dataset/2849f5c7-d153-4ff3-b5bc-30b4ee1137bb> a dcat:Dataset ;
    dct: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." ;
    dct:identifier "cec2780a-4455-408e-9bb5-b25a7154c23f" ;
    dct:issued "2025-11-20T14:25:50.981990"^^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: 5 Model prediction data" ;
    dcat:contactPoint [ a vcard:Organization ;
            vcard:fn "U.S. Geological Survey (Point of Contact)" ;
            vcard:hasEmail <mailto:jread@usgs.gov> ] ;
    dcat:distribution <https://data.amerigeoss.org/dataset/2849f5c7-d153-4ff3-b5bc-30b4ee1137bb/resource/136c98e1-4b43-4811-b171-c0b1b2e710f3>,
        <https://data.amerigeoss.org/dataset/2849f5c7-d153-4ff3-b5bc-30b4ee1137bb/resource/26d740ac-2e5e-41a4-93fb-fbd3ba8283bc>,
        <https://data.amerigeoss.org/dataset/2849f5c7-d153-4ff3-b5bc-30b4ee1137bb/resource/60904f32-21c2-4b3b-ac5d-ca2bdfeb911a>,
        <https://data.amerigeoss.org/dataset/2849f5c7-d153-4ff3-b5bc-30b4ee1137bb/resource/91d4a97c-652a-4fd4-a2cd-32f8d8835480>,
        <https://data.amerigeoss.org/dataset/2849f5c7-d153-4ff3-b5bc-30b4ee1137bb/resource/9f871e5e-49ac-435d-ac80-25ff71580533>,
        <https://data.amerigeoss.org/dataset/2849f5c7-d153-4ff3-b5bc-30b4ee1137bb/resource/c18daf72-b82e-48c2-ada6-68275e8ff97e>,
        <https://data.amerigeoss.org/dataset/2849f5c7-d153-4ff3-b5bc-30b4ee1137bb/resource/d830ff1d-8651-4189-9ebd-1e9fcde66d77>,
        <https://data.amerigeoss.org/dataset/2849f5c7-d153-4ff3-b5bc-30b4ee1137bb/resource/dc802b9d-4822-4ff8-a5d4-93eabda148f9> ;
    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-5d915c5de4b0c4f70d0ce51e",
        "water" ;
    dcat:theme <%7Bgeospatial%7D> .

<https://data.amerigeoss.org/dataset/2849f5c7-d153-4ff3-b5bc-30b4ee1137bb/resource/136c98e1-4b43-4811-b171-c0b1b2e710f3> a dcat:Distribution ;
    dct:issued "2022-08-07T11:42:54.795725"^^xsd:dateTime ;
    dct:modified "2025-11-20T14:25:50.969099"^^xsd:dateTime ;
    dct:title "Web Resource" ;
    dcat:accessURL <http://dx.doi.org/10.1029/2019WR024922> .

<https://data.amerigeoss.org/dataset/2849f5c7-d153-4ff3-b5bc-30b4ee1137bb/resource/26d740ac-2e5e-41a4-93fb-fbd3ba8283bc> a dcat:Distribution ;
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    dct:title "Web Resource" ;
    dcat:accessURL <http://dx.doi.org/10.5066/P9AQPIVD> .

<https://data.amerigeoss.org/dataset/2849f5c7-d153-4ff3-b5bc-30b4ee1137bb/resource/60904f32-21c2-4b3b-ac5d-ca2bdfeb911a> a dcat:Distribution ;
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    dct:title "Web Resource" ;
    dcat:accessURL <https://doi.org/10.5066/F7D798MJ> .

<https://data.amerigeoss.org/dataset/2849f5c7-d153-4ff3-b5bc-30b4ee1137bb/resource/91d4a97c-652a-4fd4-a2cd-32f8d8835480> a dcat:Distribution ;
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    dct:title "Web Resource" ;
    dcat:accessURL <https://doi.org/10.5194/gmd-12-473-2019> .

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

<https://data.amerigeoss.org/dataset/2849f5c7-d153-4ff3-b5bc-30b4ee1137bb/resource/c18daf72-b82e-48c2-ada6-68275e8ff97e> a dcat:Distribution ;
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    dct:format "PDF" ;
    dct:issued "2022-08-07T11:42:54.795737"^^xsd:dateTime ;
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    dct:title "PDF File" ;
    dcat:accessURL <https://arxiv.org/pdf/1810.02880.pdf> .

<https://data.amerigeoss.org/dataset/2849f5c7-d153-4ff3-b5bc-30b4ee1137bb/resource/d830ff1d-8651-4189-9ebd-1e9fcde66d77> a dcat:Distribution ;
    dct:description "The metadata original source" ;
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    dct:title "Original Metadata" ;
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    dcat:mediaType "text/xml" .

<https://data.amerigeoss.org/dataset/2849f5c7-d153-4ff3-b5bc-30b4ee1137bb/resource/dc802b9d-4822-4ff8-a5d4-93eabda148f9> a dcat:Distribution ;
    dct:description "CSDGM IMPORT ERROR: No digtinfo/formcont" ;
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    dct:modified "2025-11-20T14:25:50.968277"^^xsd:dateTime ;
    dct:title "This is the parent metadata file representing three sub-datasets" ;
    dcat:accessURL <http://dx.doi.org/10.5066/P9AQPIVD> .

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    foaf:name "US Migrating" .