OMB Individually Reported
Using Graph Neural Networks for development of nonergodic earthquake ground-motion models
Low riskExact public inventory row
Description
May ultimately enable better hazard assessment, reduces potential disaster costs, and improves the accuracy of public hazard maps and building guidelines.
Detailed example
Generates predictions of ground motion that account for local site and path effects, improving accuracy of seismic hazard models.
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
This work may help improves seismic ground motion predictions, guiding safer construction and disaster planning while reducing long-term costs.
Controls / human review
ATO: No; PIA: Not published