Carnegie Mellon University

photo of a man inspecting a gauge

January 03, 2018

Fire Risk Analysis: Predicting Fire Risk to Prioritize Commercial Property Fire Inspections

Working with the Pittsburgh Bureau of Fire (PBF), the Fire Risk Analysis team is using historical fire incident and inspection data, coupled with business permits and property condition information to develop predictive models of structure fire risk for commercial properties. PBF conducts regular fire inspections of commercial properties, as stipulated by the municipal fire code. With a prioritization method informed by the prediction probability of fire risk from machine learning models, and implemented in an interactive map visualization, they will be better able to target their inspections for the properties at greatest risk of fire.

STATUS: Our team has developed a machine learning model to predict nonresidential structure fire risk with a predictive performance better than the current state-of-the-art. We trained the model on data of historical fire incidents, property inspections and property assessments and evaluated multiple model types. The risk scores generated by the model are used to inform the Bureau of Fire’s prioritization of property fire inspections, so they can inspect the properties at greatest risk of fire. The model is currently deployed on their servers and retrains on a regular basis, as new data comes in. The output of the model is displayed in a data dashboard and interactive map used by fire inspectors and fire chiefs to inform their strategic planning. Our future work includes incorporating new data sets, experimenting with new model types, and expanding this approach to predict fire risk in residential properties at the census block level.

A final report from this phase of the project can be found here.


Pittsburgh Bureau of Fire

Pittsburgh Department of Innovation and Performance


Michael Madaio
Ph.D. Student, Human-Computer Interaction Institute, Carnegie Mellon University


Bhavkaran Singh
Qianyi Hu
Palak Narang
Nathan Kuo
Jeffrey Chen
Fangyan Chen