Item talk:Q330594

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       "name": "Worland, Scott C.",
       "nameType": "Personal",
       "givenName": "Scott C.",
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       "name": "Farmer, William H.",
       "nameType": "Personal",
       "givenName": "William H.",
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     {
       "name": "Kiang, Julie E",
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       "givenName": "Julie E",
       "familyName": "Kiang",
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   "titles": [
     {
       "title": "7Q10 records and basin characteristics for 224 basins in South Carolina, Georgia, and Alabama (2015)"
     }
   ],
   "publisher": "U.S. Geological Survey",
   "container": {},
   "publicationYear": 2017,
   "subjects": [
     {
       "subject": "USGS Data Release"
     }
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   "contributors": [],
   "dates": [
     {
       "date": "2017",
       "dateType": "Created"
     },
     {
       "date": "2017",
       "dateType": "Issued"
     }
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   "types": {
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       "relatedIdentifier": "https://doi.org/10.1016/j.envsoft.2017.12.021",
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   "descriptions": [
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       "description": "This data release provides the data and R scripts used for the 2017 submitted publication titled \"Improving predictions of hydrological low-flow indices in ungaged basins using machine learning\". There are two .csv files and 14 R-scripts included below. The lowflow_sc_ga_al_gagesII_2015.csv datafile contains the annual minimum seven-day mean streamflow with an annual exceedance probability of 90% (7Q10) for 224 basins in South Carolina, Georgia, and Alabama. The datafile also contains 231 basin characteristics from the Gages II dataset (https://water.usgs.gov/lookup/getspatial?gagesII_Sept2011). The \"all_preds.csv\" file contains the leave-one-out cross validated predictions for all the models. The paper associated with the data release compares the ability of eight machine-learning models (elastic net, gradient boosting, kernel-k-nearest neighbors, two variants of support vector machines, M5-cubist, random forest, and a meta-learning ensemble M5-cubist model) and four baseline models (ordinary kriging, a unit-area discharge model, and two variants of censored regression) to generate estimates of the 7Q10 at 224 unregulated sites in South Carolina, Georgia, and Alabama.",
       "descriptionType": "Abstract"
     }
   ],
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