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"identifier": "https://doi.org/10.5066/f7cr5s4t",
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"creators": [
{
"name": "Worland, Scott C.",
"nameType": "Personal",
"givenName": "Scott C.",
"familyName": "Worland",
"affiliation": [],
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"schemeUri": "https://orcid.org",
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},
{
"name": "Farmer, William H.",
"nameType": "Personal",
"givenName": "William H.",
"familyName": "Farmer",
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"schemeUri": "https://orcid.org",
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},
{
"name": "Kiang, Julie E",
"nameType": "Personal",
"givenName": "Julie E",
"familyName": "Kiang",
"affiliation": [],
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"schemeUri": "https://orcid.org",
"nameIdentifier": "https://orcid.org/0000-0003-0653-4225",
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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"
}
],
"contributors": [],
"dates": [
{
"date": "2017",
"dateType": "Created"
},
{
"date": "2017",
"dateType": "Issued"
}
],
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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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