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.

Data and Resources

Field Value
accessLevel public
bureauCode {010:12}
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catalog_describedBy https://project-open-data.cio.gov/v1.1/schema/catalog.json
identifier USGS:5f32c66b82cee144fb313867
metadata_type geospatial
modified 20220505
old-spatial -96.4246, 29.7392, -84.7824, 38.6976
publisher U.S. Geological Survey
publisher_hierarchy Department of the Interior > U.S. Geological Survey
resource-type Dataset
source_datajson_identifier true
source_hash fd6462b2ee1e893ef079ba3fabda81157959de4f
source_schema_version 1.1
spatial {"type": "Polygon", "coordinates": [[[-96.4246, 29.7392], [-96.4246, 38.6976], [ -84.7824, 38.6976], [ -84.7824, 29.7392], [-96.4246, 29.7392]]]}
theme {geospatial}
Groups
  • AmeriGEOSS
  • National Provider
  • North America
Tags
  • amerigeo
  • amerigeoss
  • arkansas
  • baseflow
  • ckan
  • geo
  • geoss
  • inlandwaters
  • louisiana
  • mississippi
  • mississippi-river-delta
  • missouri
  • national
  • north-america
  • statistical-analysis
  • streamflow
  • tennessee
  • united-states
  • usgs-5f32c66b82cee144fb313867
isopen False
license_id notspecified
license_title License not specified
maintainer Stephen M Westenbroek
maintainer_email smwesten@usgs.gov
metadata_created 2025-11-21T08:21:57.480053
metadata_modified 2025-11-21T08:21:57.480057
notes 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.
num_resources 2
num_tags 19
title 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