OMB Individually Reported
Machine Learning Refines Quagga Habitat Suitability [2024 INV#DOI-74 (NEW)]
Low riskExact public inventory row
Description
Invasive mussels pose a number of challenges for the continuous operation of water infrastructure.
Detailed example
Using AI/ML methods, habitat variables were identified that may be limiting mussel establishment at reservoirs and may be influencing sudden declines in mussel populations at infested reservoirs.
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
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
Understanding habitat suitability can inform management actions.
Controls / human review
ATO: No; PIA: Not published