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USGS Data Source
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Model Archive and Data Release: Input data, trained model data, and model outputs for predicting streamflow and base flow for the Mississippi Embayment Regional Study Area using a random forest model
This data archive contains datasets developed for the purpose of training and applying random forest models to the Mississippi Embayment Regional Aquifer. The random forest models are designed to predict total stream flow and baseflow as a function of a combination of watershed characteristics and monthly weather data. These datasets are associated with a report (SIR 2022-xxxx) and code contained in a USGS GitLab repository. The GitLab repository (https://code.usgs.gov/map/maprandomforest/) contains much more information about how these data may be used to supply predictions of stream flow and baseflow.
| Author(s) |
Stephen M Westenbroek  ,
Benjamin J Dietsch ,
Brian K. Breaker
|
| Publication Date | 2022-05-05 |
| Beginning Date of Data | 1895 |
| Ending Date of Data | 2019 |
| Data Contact | |
| DOI | https://doi.org/10.5066/P9QCK8HY |
| Citation | Westenbroek, S.M., Dietsch, B.J., and Breaker, B.K., 2022, Model Archive and Data Release: Input data, trained model data, and model outputs for predicting streamflow and base flow for the Mississippi Embayment Regional Study Area using a random forest model: U.S. Geological Survey data release, https://doi.org/10.5066/P9QCK8HY. |
| Metadata Contact | |
| Metadata Date | 2022-05-05 |
| Related Publication | Loading... |
| Citations of these data | Loading https://doi.org/10.3133/sir20225079 |
| Access | public |
| License | http://www.usa.gov/publicdomain/label/1.0/ |
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Harvest Source: ScienceBase
Harvest Date: 2024-07-18T13:40:47.875Z
Harvest Date: 2024-07-18T13:40:47.875Z