Using machine learning to improve predictions and provide insight into fluvial sediment transport

J. William Lund, Joel T. Groten, Diana L. Karwan, Chad Babcock

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

A thorough understanding of fluvial sediment transport is critical to addressing many environmental concerns such as exacerbated flooding, degradation of aquatic habitat, excess nutrients, and the economic challenges of restoring aquatic systems. Fluvial sediment samples are integral for addressing these environmental concerns but cannot be collected at every river and time of interest. Therefore, to gain a better understanding for rivers where direct measurements have not been made, extreme gradient boosting machine learning (ML) models were developed and trained to predict suspended sediment and bedload from sampling data collected in Minnesota, United States (U.S.), by the U.S. Geological Survey. Approximately 400 watershed (full upstream area), catchment (nearby landscape), near-channel, channel, and streamflow features were retrieved or developed from multiple sources, reduced to approximately 30 uncorrelated features, and used in the final ML models. The results indicate suspended sediment and bedload ML models explain approximately 70% of the variance in the datasets. Important features used in the models were interpreted with Shapley additive explanation (SHAP) plots, which provided insight into sediment transport processes. The most important features in the models were developed to normalize streamflow by the 2-year recurrence interval and quantify the rate of change in streamflow (slope), which helped account for sediment hysteresis. Generally, this study also showed a combination of mostly watershed and catchment geospatial features were important in ML models that predict sediment transport from physical samples. This study is a promising step forward in making fluvial sediment transport predictions using machine learning models trained by physically collected samples. The approach developed here can be used wherever similar datasets exists and will be useful for landscape and water management.

Original languageEnglish (US)
Article numbere14648
JournalHydrological Processes
Volume36
Issue number8
DOIs
StatePublished - Aug 2022

Bibliographical note

Funding Information:
The authors would like to thank the Minnesota Department of Natural Resources, Minnesota Pollution Control Agency, Minnesota's Clean Water Fund, and U.S. Geological Survey Cooperative Matching Funds for their financial assistance with this study. J. William Lund was partially supported by the Peter F. Ffolliott Fellowship in the Department of Forest Resources, University of Minnesota and USGS Cooperative Agreement G20AC00427. Diana L. Karwan acknowledges support from the Minnesota Agricultural Research Station (Project MIN-42-080). Christopher Ellison, Erin Coenen, and Gerald Storey of the U.S. Geological Survey are acknowledged for assistance with project planning, data collection, and database management. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

Funding Information:
The authors would like to thank the Minnesota Department of Natural Resources, Minnesota Pollution Control Agency, Minnesota's Clean Water Fund, and U.S. Geological Survey Cooperative Matching Funds for their financial assistance with this study. J. William Lund was partially supported by the Peter F. Ffolliott Fellowship in the Department of Forest Resources, University of Minnesota and USGS Cooperative Agreement G20AC00427. Diana L. Karwan acknowledges support from the Minnesota Agricultural Research Station (Project MIN‐42‐080). Christopher Ellison, Erin Coenen, and Gerald Storey of the U.S. Geological Survey are acknowledged for assistance with project planning, data collection, and database management. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

Publisher Copyright:
© 2022 The Authors. Hydrological Processes published by John Wiley & Sons Ltd. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Keywords

  • bedload
  • hysteresis
  • machine learning
  • sediment rating curve
  • sediment transport
  • suspended sediment

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