{
"DOI": {
"doi": "10.5066/p9niiejj",
"identifiers": [],
"creators": [
{
"name": "Sarah M Banks",
"nameType": "Personal",
"affiliation": [
"United States Geological Survey"
],
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{
"name": "Caleb J. DeAbreu",
"nameType": "Personal",
"affiliation": [
"United States Geological Survey"
],
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},
{
"name": "Victor L Roland",
"nameType": "Personal",
"affiliation": [
"United States Geological Survey"
],
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"schemeUri": "https://orcid.org",
"nameIdentifier": "https://orcid.org/0000-0002-6260-9351",
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{
"name": "William H Asquith",
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"United States Geological Survey"
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"nameIdentifier": "https://orcid.org/0000-0002-7400-1861",
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},
{
"name": "Kirk D Rodgers",
"nameType": "Personal",
"affiliation": [
"United States Geological Survey"
],
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"schemeUri": "https://orcid.org",
"nameIdentifier": "https://orcid.org/0000-0003-4322-2781",
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]
}
],
"titles": [
{
"title": "Modeled daily salinity derived from multiple machine learning methodologies and generalized additive models for three salinity monitoring sites in Mobile Bay, northern Gulf of Mexico, 1980\u20132021"
}
],
"publisher": "U.S. Geological Survey",
"container": {},
"publicationYear": 2024,
"subjects": [
{
"subject": "water resources"
}
],
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"descriptions": [
{
"description": "Results from generalized additive models (GAM), random forest models (RFM), and cubist models (CUB) for three Dauphin Island Sealab (DIS) operated salinity sites in Mobile Bay are reported in this data release. These sites included Meaher Park (DIS:MHPA1), Middle Bay Lighthouse (DIS:MBLA1), and Dauphin Island (DIS:DPIA1). The constructed models predicted a 42-year daily salinity record from 1980 to 2021 at each site based on incomplete imputed salinity records and several explanatory variables. Explanatory variables included: daily streamflow from 8 United States Geological Survey (USGS) streamgages, daily minimum and maximum temperature, precipitation, vapor pressure, wind speed, wind direction, horizontal and vertical wind speed lagged from 0 to 7 days, altitude and azimuth of the sun and moon, and the positive and negative slopes of streamflow change over the previous seven days. Two GAM, RFM, and CUB salinity models were developed for each site using even- and odd-year-holdout. The final predicted salinity time series were derived from inverse error weighted pooling of the even- and odd-year model results for each model type. A similar methodology was used to pool the even- and odd-year models from the three model types to create a time series of daily salinity predictions from the ensemble of models. By applying model tests, prediction intervals estimations for the GAM, RFM, CUB were determined with model ensemble pooled predictions as shown in model input. Model input even- and odd-year models, helped determine pooling predictions and prediction intervals. RFM and CUB models displayed variable importance along with variable significance as seen in the GAM model. Predicted salinity levels exhibit variation from measured values, with certain maximum salinity predictions potentially exceeding the natural conditions expected in Mobile Bay.",
"descriptionType": "Abstract"
}
],
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"url": "https://www.sciencebase.gov/catalog/item/64b04e5cd34e70357a2975b2",
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"created": "2024-04-22T15:21:32Z",
"registered": "2024-04-22T15:21:33Z",
"published": null,
"updated": "2024-06-04T15:35:06Z"
}
}