Coastal Change Likelihood: Synthesizing change factors using supervised learning
Description
A supervised machine-learning framework (support vector machine algorithm) is used to predict future decadal-scale coastal change and its primary driver by combining over 20 published coastal geospatial datasets that describe the coastal landscape and the hazards that affect it.
Detailed example
The system produces maps of future coastal change, along with an indicator of the primary hazard(s) that produce the estimated change.
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
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
The Coastal Change Likelihood project is a computer-aided synthesis of the factors that determine future coastal landscape change that can be used by decision makers to support adaptation, mitigation, and prioritization of coastal zone resources and infra
Controls / human review
ATO: Yes; PIA: Not published
Data needed
NOAA’s CCAP, NAIP imagery. Also data from the National Aerial Imagery Program. See publication for more information on performance evaluation: https://doi.org/10.2112/JCOASTRES-D-24-00072.1