Soil Moisture Modeling

Overview

Development stage

PilotSource: b) Pilot – The use case has been deployed in a limited test or pilot capacity.

High-impact designation

Not high-impactSource: c) Not high-impact

Impact justification

This AI use case does not serve as a principal basis for decisions or actions with legal, material, and binding

Start date

2022-02-01 (day precision)Source: 2022-02-01T00:00:00

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

Machine learning solves issues with soil moisture by enhancing accuracy and efficiency in data analysis

Expected benefits

The ability to determine evapotranspiration rates on disposal cell cover using publicly available data from satellites.

System outputs

Multi-layer soil moisture model/prediction

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)

LMGSS

Custom-developed code

Yes

Public source code

Not reported

Sourcing

How it was built

Purchased from vendorSource: a) Purchased from a vendor

Vendor (as reported)

University of Montana

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

Data is held back from the models to validate model outputs.

Federal Data Catalog

Not reported

Governance

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

Potential impacts and how they were identified

Improved management of the remedy