Using Machine Learning in Minnesota’s StreamStats to Predict Fluvial Sediment

Fact Sheet 2025-3005
Prepared in cooperation with the Minnesota Pollution Control Agency
By: , and 

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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
Additional publication details