DOI · DOI-0115 · record R2645

Coastal Change Likelihood: Synthesizing change factors using supervised learning

Department of the Interior · USGS

Overview

Development stage: DeployedSource: c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: Not reported
Start date: 2023-02-28 (day precision)Source: 2/28/2023
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Science
Operational functions: No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

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.

Expected benefits

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

System outputs

The system produces maps of future coastal change, along with an indicator of the primary hazard(s) that produce the estimated change.

Technology

AI classification: Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…
System name(s): Not reported
Custom-developed code: No
Public source code: Not reported

Sourcing

How it was built: Purchased from vendorSource: a) Purchased from a vendor
Vendor (as reported): ESRI
Vendors (standardized): Esri

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII: No
Privacy Impact Assessment: Not reported
Authorization to Operate: Yes
Demographic features: k) None of the above
Training and evaluation data: 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
Federal Data Catalog: www.sciencebase.gov ↗

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Pre-deployment testingNo answer reported

AI impact assessmentNo answer reported

Independent reviewNo answer reported

Ongoing monitoringNo answer reported

Operator trainingNo answer reported

Fail-safeNo answer reported

Appeal processNo answer reported

User and public consultationNo answer reported

Potential impacts and how they were identified

Not reported