Using Machine Learning in Minnesota’s StreamStats to Predict Fluvial Sediment
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Abstract
A thorough understanding of fluvial sediment transport is essential for addressing key environmental issues such as aquatic habitat degradation, flooding, excess nutrients, and challenges with river restoration. Fluvial sediment samples are valuable for addressing these concerns, but their collection is often impractical across all rivers and timeframes of interest. In addition, previously used analytical and numerical methods have not allowed for the transfer of knowledge from sites that have data to sites that do not have data. To overcome this limitation, the U.S. Geological Survey developed machine learning models to predict suspended-sediment concentrations and bedload transport in Minnesota rivers that lack physical sediment data and integrated them into the U.S. Geological Survey StreamStats web application.
Suggested Citation
Groten, J.T., Lund, J.W., Coenen, E.N., Medenblik, A.S., Wavra, H.N., Kennedy, M., and Johnson, G.D., 2025, Using machine learning in Minnesota’s StreamStats to predict fluvial sediment: U.S. Geological Survey Fact Sheet 2025–3005, 4 p., https://doi.org/10.3133/fs20253005.
ISSN: 2327-6932 (online)
Study Area
Table of Contents
- Introduction
- Objective
- Machine Learning Models for Fluvial Sediment Prediction
- StreamStats Integration
- Sediment Monitoring in Minnesota
- Summary
- Acknowledgements
- References Cited
Publication type | Report |
---|---|
Publication Subtype | USGS Numbered Series |
Title | Using machine learning in Minnesota’s StreamStats to predict fluvial sediment |
Series title | Fact Sheet |
Series number | 2025-3005 |
DOI | 10.3133/fs20253005 |
Year Published | 2025 |
Language | English |
Publisher | U.S. Geological Survey |
Publisher location | Reston, VA |
Contributing office(s) | Upper Midwest Water Science Center |
Description | 4 p. |
Country | United States |
State | Minnesota |
Online Only (Y/N) | Y |
Additional Online Files (Y/N) | N |
Google Analytic Metrics | Metrics page |