DOI · DOI-0139 · record R2619

Predicting from the past - identifying characteristics of invasion-resistant and invasion-prone waterbodies to aid horizon scanning

Department of the Interior · USGS

Overview

Development stage: In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: Not reported
Start date: Not reported
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

Machine learning and statistical modeling will be used to leverage region-wide waterbody invasion histories and datasets on the physical, biological, chemical, anthropogenic, and geographic characteristics of these waterbodies to: a) identify those variables that increase or decrease invasion risk, b) categorize all waterbodies in the region based on their invasion risk, and c) provide a decision support tools for managers and policy makers to identify at-risk sites.

Expected benefits

AI/ML will provide a decision support tool for managers and policy makers to identify waterbodies vulnerable to invasion by non-native fishes and potential actions that could be implemented to reduce risk of invasion.

System outputs

Waterbodies vulnerable to invasion

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 yet available

Sourcing

How it was built: Not reported
Vendor (as reported): Not reported
Vendors (standardized): None on the standard list

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: No
Demographic features: k) None of the above
Training and evaluation data: Not reported
Federal Data Catalog: Not reported

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