Item talk:Q317798

{

 "DOI": {
   "doi": "10.5066/p13ggmtz",
   "identifiers": [],
   "creators": [
     {
       "name": "Courtney D Killian",
       "nameType": "Personal",
       "affiliation": [
         "United States Geological Survey"
       ],
       "nameIdentifiers": [
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           "schemeUri": "https://orcid.org",
           "nameIdentifier": "https://orcid.org/0000-0002-2137-2722",
           "nameIdentifierScheme": "ORCID"
         }
       ]
     },
     {
       "name": "William H Asquith",
       "nameType": "Personal",
       "affiliation": [
         "United States Geological Survey"
       ],
       "nameIdentifiers": [
         {
           "schemeUri": "https://orcid.org",
           "nameIdentifier": "https://orcid.org/0000-0002-7400-1861",
           "nameIdentifierScheme": "ORCID"
         }
       ]
     }
   ],
   "titles": [
     {
       "title": "Statistical predictions of groundwater levels and related spatial diagnostics for the Mississippi River Valley alluvial aquifer from the mmlMRVAgen1 statistical machine-learning software"
     }
   ],
   "publisher": "U.S. Geological Survey",
   "container": {},
   "publicationYear": 2024,
   "subjects": [
     {
       "subject": "Hydrology"
     }
   ],
   "contributors": [],
   "dates": [],
   "language": null,
   "types": {
     "ris": "DATA",
     "bibtex": "misc",
     "citeproc": "dataset",
     "schemaOrg": "Dataset",
     "resourceType": "Dataset",
     "resourceTypeGeneral": "Dataset"
   },
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   "version": null,
   "rightsList": [
     {
       "rights": "Creative Commons Zero v1.0 Universal",
       "rightsUri": "https://creativecommons.org/publicdomain/zero/1.0/legalcode",
       "schemeUri": "https://spdx.org/licenses/",
       "rightsIdentifier": "cc0-1.0",
       "rightsIdentifierScheme": "SPDX"
     }
   ],
   "descriptions": [
     {
       "description": "A multiple machine-learning model (Asquith and Killian, 2024) implementing Cubist and Random Forest regressions was used to predict monthly mean groundwater levels through time for the available years described in the metadata for the Mississippi River Valley alluvial aquifer (MRVA). The MRVA is\u00a0the surficial aquifer of the Mississippi Alluvial Plain (MAP), located in the south-central United States. Employing two machine-learning techniques offered the opportunity to generate model and statistical error and covariance between them to estimate total uncertainty. Potentiometric surface predictions were made at the 1-kilometer grid scale using the National Hydrogeologic Grid (Clark and others, 2018).\nFor a full description of covariate assemblage and hydrograph modeling, see\u00a0Asquith and Killian (2022) (covMRVAgen1 software). For a full description of multiple machine-learning modeling, see\u00a0Asquith and Killian (2024) (mmlMRVAgen1 software).\u00a0\n",
       "descriptionType": "Abstract"
     }
   ],
   "geoLocations": [],
   "fundingReferences": [],
   "url": "https://www.sciencebase.gov/catalog/item/65ea250ad34ed433ba12f139",
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   "created": "2024-08-30T19:22:28Z",
   "registered": "2024-08-30T19:22:28Z",
   "published": null,
   "updated": "2024-08-30T19:25:00Z"
 }

}