Extreme gradient boosting machine learning models, suspended sediment, bedload, streamflow, and geospatial data, Minnesota, 2007-2019
A series of machine learning (ML) models were developed for Minnesota. The ML models were trained and tested using suspended sediment, bedload, streamflow, and geospatial data to predicted suspended sediment and bedload. Suspended sediment, bedload, and streamflow data were collected during water years 2007 through 2019. The ML models were used to improve understanding of sediment transport processes and increase accuracy of estimating sediment and loads for streams and rivers across Minnesota. The contents of this data release include README files, input files, output files, and source code (R software version 3.6.1) needed to reproduce the ML models and results in the associated article in Hydrological Processes (https://doi.org/10.1002/hyp.14648).
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Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[ "010:12" ] |
| contactPoint |
{ "fn": "J. William Lund", "@type": "vcard:Contact", "hasEmail": "mailto:jlund@usgs.gov" } |
| description | A series of machine learning (ML) models were developed for Minnesota. The ML models were trained and tested using suspended sediment, bedload, streamflow, and geospatial data to predicted suspended sediment and bedload. Suspended sediment, bedload, and streamflow data were collected during water years 2007 through 2019. The ML models were used to improve understanding of sediment transport processes and increase accuracy of estimating sediment and loads for streams and rivers across Minnesota. The contents of this data release include README files, input files, output files, and source code (R software version 3.6.1) needed to reproduce the ML models and results in the associated article in Hydrological Processes (https://doi.org/10.1002/hyp.14648). |
| distribution |
[ { "@type": "dcat:Distribution", "title": "Digital Data", "format": "XML", "accessURL": "https://doi.org/10.5066/P9VOPSEJ", "mediaType": "application/http", "description": "Landing page for access to the data" }, { "@type": "dcat:Distribution", "title": "Original Metadata", "format": "XML", "mediaType": "text/xml", "description": "The metadata original format", "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.61572adcd34e0df5fb9f8300.xml" } ] |
| identifier | http://datainventory.doi.gov/id/dataset/USGS_61572adcd34e0df5fb9f8300 |
| keyword |
[ "USGS:61572adcd34e0df5fb9f8300", "bedload", "sediment transport", "suspended material (water)" ] |
| modified | 2022-08-11T00:00:00Z |
| publisher |
{ "name": "U.S. Geological Survey", "@type": "org:Organization" } |
| spatial | -97.5929662585258, 43.1421712434946, -89.4191381335258, 49.4476743069444 |
| theme |
[ "geospatial" ] |
| title | Extreme gradient boosting machine learning models, suspended sediment, bedload, streamflow, and geospatial data, Minnesota, 2007-2019 |
