OMB Individually Reported
Remote sensing of particulate and filter passing mercury species: models and proxies
Low riskExact public inventory row
Description
AI provides the framework for understanding the relationship between optical water quality parameters and non-optical contaminants to develop highly accurate remote sensing models for mercury from satellite images and field measurements
Detailed example
A model that is applied to remote sensing imagery to provide a measurement of mercury and meythlmercury in water bodies
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
Synoptic, spatially cohesive maps of mercury distribution in surface water
Controls / human review
ATO: No; PIA: Not published