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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, 1980–2021

Metadata Updated: July 20, 2024

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.

Access & Use Information

Public: This dataset is intended for public access and use. License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

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Dates

Metadata Created Date July 20, 2024
Metadata Updated Date July 20, 2024

Metadata Source

Harvested from DOI EDI

Additional Metadata

Resource Type Dataset
Metadata Created Date July 20, 2024
Metadata Updated Date July 20, 2024
Publisher U.S. Geological Survey
Maintainer
@Id http://datainventory.doi.gov/id/dataset/c93d4776849bad21d8b4d0b649994cfc
Identifier USGS:64b04e5cd34e70357a2975b2
Data Last Modified 20240604
Category geospatial
Public Access Level public
Bureau Code 010:12
Metadata Context https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld
Metadata Catalog ID https://datainventory.doi.gov/data.json
Schema Version https://project-open-data.cio.gov/v1.1/schema
Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
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Harvest Source Id 52bfcc16-6e15-478f-809a-b1bc76f1aeda
Harvest Source Title DOI EDI
Metadata Type geospatial
Old Spatial -89.37864,30.179799,-87.340229,30.763887
Publisher Hierarchy White House > U.S. Department of the Interior > U.S. Geological Survey
Source Datajson Identifier True
Source Hash a671c00b5d191b8f8691f13dc7df8ca3a8f1ba7aae570739935e38395eb6085f
Source Schema Version 1.1
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