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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: January 21, 2026

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 January 11, 2026
Metadata Updated Date January 21, 2026

Metadata Source

Harvested from DOI USGS DCAT-US

Additional Metadata

Resource Type Dataset
Metadata Created Date January 11, 2026
Metadata Updated Date January 21, 2026
Publisher U.S. Geological Survey
Maintainer
Identifier http://datainventory.doi.gov/id/dataset/USGS_64b04e5cd34e70357a2975b2
Data Last Modified 2024-06-04T00:00:00Z
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://ddi.doi.gov/usgs-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
Datagov Dedupe Retained 20260121130127
Harvest Object Id dc3f0cbd-1c0d-4133-abc8-bab2e47dd824
Harvest Source Id 2b80d118-ab3a-48ba-bd93-996bbacefac2
Harvest Source Title DOI USGS DCAT-US
Metadata Type geospatial
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Source Datajson Identifier True
Source Hash 7403e0900a813d6d8f44301408ef01f86f32107ef3425093aea3ea6c7b857637
Source Schema Version 1.1
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