Public Assistance Workload Projections
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 Public Assistance, predicting the number of PA projects that applicants will submit, predicting the number of sites that will need to be inspected per PA project, predicting the cost of delivering assistance, etc. 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
Supervised learning models produce predictions, not recommendations and not decisions (though they can be used to inform human users in making recommendations and decisions)._x000D_ _x000D_ A minimum, these supervised learning models will produce point predictions for the different quantities of interest for disaster declarations. Additionally supervised learning models may produce prediction intervals or predictive distributions as feasible and appropriate for the given prediction problem. Often these outputs will be shared via business intelligence tools (e.g., Tableau or PowerBI) for wide internal FEMA use. Some predictions may be shared to a more restricted audience through simpler means (e.g., an excel workbook) as appropriate.
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
1. For informational purposes: the models will produce predictions for to-be-determined quantities of interest. These quantities are often of interest to Agency personnel in the field, region, and headquarters, as well as DHS, OMB, NSC, and the White House._x000D_ 2. For decisional purposes: in addition to being informative, the model’s predictions are likely to be used for decision making. Projections help inform staffing levels and timing.
Controls / human review
ATO: No; PIA: Not published
Data needed
FEMA: Historical Declaration and Public Assistance activity data_x000D_ U.S. Census: Housing Units Logged, Density Housing Units, Number City/Township Govs, Number of Special District Govs_x000D_ DHS Infrastructure: Fire Stations, Electric Substations, Dams, Ten Mile Power Lines_x000D_ Dept of Agriculture: Agricultural Land (sq. miles), Wetland (sq. miles), Developed Land (sq. miles)