Using AI to Prevent Fire Risks
Metro21’s project, Fire Risk Analysis: Predicting Fire Risk to Prioritize Commercial Property Fire Inspections, was featured in an article by GCN for their work of building a Fire Risk Analysis model that uses machine learning to predict the likelihood of a fire occurring at a given property.
This project was implemented in the City of Pittsburgh in February 2018 and re-evaluated six months later. The team at Carnegie Mellon University found that in that six-month window, there were about 45 fire incidents, one-third of which occurred in buildings the model ranked as high-risk by their model.
“What that tells us and the fire inspectors also is that they should be using this set of high-risk properties to inform where they’re inspecting,” said Michael Madaio, a graduate-research assistant at CMU’s Human-Computer Interaction Institute who runs the project.
The fire chief and inspectors had data from checking properties’ adherence to safety codes. They also had data on fires and where incidents were occurring “but they didn’t quite know whether they were targeting the properties that needed those inspections most.” he said.
The model generates a likelihood of an event occurring on a scale of 1 to 10, and the results are integrated into the city’s existing open data platform called Burgh’s Eye View.
“It’s an interactive map dashboard. Various city agencies already use that for things like monitoring 311 calls or other alerts and city data. That was something that the fire department was already using, so we integrated these property risk scores into that existing interactive map,” he said.
To view to full article, click here.
To learn more about this project, click here.