Lund, J.W. and Groten, J.T., 2022, Extreme gradient boosting machine learning models, suspended sediment, bedload, streamflow, and geospatial data, Minnesota, 2007-2019: U.S. Geological Survey data release, https://doi.org/10.5066/P9VOPSEJ.
Summary
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). [...]
Summary
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).
These machine learning models were developed to increase the accuracy of estimating suspended sediment and bedload in Minnesota’s streams and rivers. The models and associated files are included and documented in this data release and in the associated article in Hydrological Processes (https://doi.org/10.1002/hyp.14648).
