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

Name: 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
Creator: Department of the Interior
Published: 2026-01-10T12:43:56+08:00

data.gov2026-01-10 收录

盐度预测

机器学习环境建模

数据链接：

https://catalog.data.gov/dataset/modeled-daily-salinity-derived-from-multiple-machine-learning-methodologies-and-generalize-98540 数据链接链接失效反馈

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资源简介：

Results from generalized additive models (GAM), random forest models (RFM), and cubist models (CUB) for three Dauphin Island Sealab (DIS) operated salinity sites in...

提供机构：

Department of the Interior

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

资源简介：

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