EPA · 77 · record R2052
Using machine learning methods to analyze drivers of water quality
Environmental Protection Agency · AO
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
- The output of this AI use case does not serve as a principal basis for decisions or actions that have a legal, material, binding, or significant effect on rights or safety.
- Start date
- 2024-01-01 (day precision)Source: 01/01/2024 00:00:00
- Withheld from public reporting
- NoSource: a) No
Mission
- Topic area
- Energy and the Environment
- Operational functions
- No function tag matched Derived by keyword rules; see methodology
Problem the AI is intended to solve
To increase our understanding of drivers of water quality.
Expected benefits
Use of these methods will help better inform water quality management activities.
System outputs
Predicted measures of water quality (e.g., concentrations or HABs) and violations.
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
- Yes
- Public source code
- Not reported
Sourcing
- How it was built
- Developed in-houseSource: b) Developed in-house
- 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
- Variables related to watershed land use, nutrient inputs, socioeconomic factors, climate, and other parameters
- 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