Individual Assistance (IA) Predictive Models for Program Quantities
Description
The use case is predicting recovery program quantities of interest using supervised learning models to include predicting the number of applicants who will apply for Individual Assistance, how many inspections will be issued, and how many units are required for direct housing. Supervised learning models include but are not limited to the use of sample statistics, generalized linear models, decision trees, and deep neural networks for the purpose of predicting unknown quantities.
Detailed example
The outputs are the predicted values for the quantities of interest, e.g. number of survivors who will register for assistance, number of inspections issued, etc.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
The models are intended to quickly quantify and reduce uncertainty around key quantities of interest to enable better programmatic decision making, such as workload management, pre-placement of staff, etc.
Controls / human review
ATO: No; PIA: Not published
Data needed
Historical data obtained from the National Emergency Management Information System (NEMIS); _x000D_ Decennial Census and American Community Survey household data