USDA · USDA-082 · record R1381

Predictive flood modeling

Department of Agriculture · Natural Resources and Environment

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: 2025-10-01 (day precision)Source: 10/1/2025
Withheld from public reporting: Not reported

Mission

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

Problem the AI is intended to solve

The model is intended to solve the overtopping and/or failure of at-risk road culverts (drains) due to projected future extreme weather induced flooding, by identifying potential undersized culverts prior to field visit.

Expected benefits

Reducing risks to forest access following future flooding and fire. Reducing costs associated with road management and repair besides the restoration costs of the drains themselves.

System outputs

The model outputs are predicted water and sediment release during floods caused by future extreme weather to determine the vulnerability level of culverts (drains).

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): CULVERT
Custom-developed code: Yes
Public source code: github.com ↗

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: High resolution precipitation, streamflow, and downscaled future precipitation.
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