Random Forest with Aquatic Effectiveness Monitoring
Description
Protecting aquatic ecosystems and restoring watershed processes can be improved by understanding predictors of sediment in streams after wildfire.
Detailed example
Outputs identified predictors for protecting aquatic ecosystems and restoring watershed processes
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
b) Pilot – The use case has been deployed in a limited test or pilot capacity.
Expected benefit
Aids in protecting aquatic ecosystems and restoring watershed processes.
Controls / human review
ATO: No; PIA: Not published
Data needed
Physical stream habitat datasets from the Aquatic Riparian Effectiveness Monitoring Program. Data about watershed conditions from StreamCAT. Fire history data from Monitoring Trends in Burn Severity (MTBS).