Urban Fire Risk Assessment

Predictive Fire Incident Risk Scoring System aims to develop a comprehensive risk assessment tool tailored to urban neighborhoods. Utilizing a combination of Census data, historical fire incident records, building structures, and expert knowledge, the project seeks to create a predictive risk scoring system capable of quantifying the overall fire incident risk level for each neighborhood. By analyzing diverse datasets and employing machine learning algorithms, the system aims to provide valuable insights into urban fire risks, enabling proactive measures to enhance public safety. Collaboration with Darkhorse Emergency presents an opportunity to expand expertise in public safety assessment and offer innovative solutions. Integration of the resulting model into Darkhorse’s data analysis software tools will enhance the company’s market presence and benefit fire agencies across North America.

Faculty Supervisor:

Nooshin Salari;Borzou Rostami

Student:

Partner:

Darkhorse Emergency

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

McMaster University; University of Alberta

Program: