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
May 10, 2022
This data release contains datasets developed for the purpose of training and applying random forest models to the Mississippi Embayment Regional Study Area. 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.
Citation Information
| Publication Year | 2022 |
|---|---|
| Title | 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 |
| DOI | 10.5066/P9QCK8HY |
| Authors | Stephen M Westenbroek, Benjamin J Dietsch, Brian K. Breaker |
| Product Type | Data Release |
| Record Source | USGS Asset Identifier Service (AIS) |
| USGS Organization | Upper Midwest Water Science Center |
| Rights | This work is marked with CC0 1.0 Universal |
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