Phys.org

Machine learning approach simulates geochemical element concentrations in rocks and stream sediments

Published in Ore Geology Reviews, their work uses machine learning to address the challenges posed by limited geochemical data. Geochemical data play a crucial role in various scientific domains and ...
US Geological Survey

Predictive understanding of stream salinization in a developed watershed using machine learning

Authors Jared David Smith, Lauren Elizabeth Koenig, Margaux Jeanne Sleckman, Alison P. Appling, Jeffrey M Sadler, Vincent T. DePaul, Zoltan Szabo ...
Nature

Machine Learning Techniques for Streamflow Forecasting

Accurate streamflow forecasting is critical for water resources management, flood mitigation and hydropower operations worldwide. Traditional hydrological models based on physical processes often ...
US Geological Survey

Machine learning generated streamflow drought forecasts for the Conterminous United States (CONUS): Developing and evaluating an operational tool to enhance sub-seasonal to ...

Authors John C. Hammond, Phillip J. Goodling, Jeremy Alejandro Diaz, Hayley R. Corson-Dosch, Aaron Joseph Heldmyer, Scott Douglas Hamshaw, Ryan R. McShane, Jesse ...