DOI · DOI-0143 · record R2616

Predictive AI applications for estimating water quality constituents as causal factors of harmful algal blooms.

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

Ensemble regressions to predict suspended sediment, total nitrogen, total phosphorus, algal pigments, and algal cell abundances and image-based estimation of suspended sediment concentration via machine learning used in combination with custom developed python code to help build on our correlative understanding of what drives harmful algal blooms and introduces a technique to better understand and attribute causality.

Expected benefits

Better understanding of the causes of harmful algal blooms in order for localities and state agencies to take proactive action to prevent harmful algal blooms that potentially have impact on life and property.

System outputs

Estimates of water quality constituents: suspended sediment, total nitrogen, total phosphorus, algal pigments, and algal cell abundances.

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: www.python.org ↗

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: No privacy use case
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