This Master of Environmental Data Science (MEDS) Capstone Project Presentation features a machine learning algorithm designed to interpret RHESSys output and extract meaningful insights into the possible impacts of climate change on forest health, and visualize findings in an interactive manner that is accessible to forest managers, students, and the general public.
A Reproducible Machine Learning Approach for Interpreting Ecohydrologic Model Outputs
Thursday, May 26 2022, 1:10-1:30
Bren Hall 1414 + Livestream
Student group: Alex Clippinger, Wylie Hampson, Shale Hunter, Peter Menzies
Faculty Advisor: Christina (Naomi) Tague