Predicting post-fire tree mortality

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

2021-10-01 (day precision)Source: 10/1/2021

Withheld from public reporting

NoSource: a) No

Mission

Topic area

Science

Operational functions

Forecasting and prediction Derived by keyword rules; see methodology

Problem the AI is intended to solve

We used random forests to select predictor variables for models of individual tree mortality following fire.

Expected benefits

These science products strengthen wildland fire and timber management practices.

System outputs

Predictive models of postfire tree survivorship.

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 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

A database of fire effects to describe the determinants of fire-induced tree mortality.

Federal Data Catalog

research.fs.usda.gov ↗

Governance

0 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

Not reported