Statistical and Machine Learning Models for Predicting Fire and Other
  Emergency Events
Dilli Prasad Sharma Nasim Beigi-Mohammadi Hongxiang Geng Dawn Dixon Rob Madro Phil Emmenegger Carlos Tobar Jeff Li Alberto Leon-Garcia
Emergency events in a city cause considerable economic loss to individuals,
their families, and the community. Accurate and timely prediction of events can
help the emergency fire and rescue services in preparing for and mitigating the
consequences of emergency events. In this paper, we present a systematic
development of predictive models for various types of emergency events in the
City of Edmonton, Canada. We present methods for (i) data collection and
dataset development; (ii) descriptive analysis of each event type and its
characteristics at different spatiotemporal levels; (iii) feature analysis and
selection based on correlation coefficient analysis and feature importance
analysis; and (iv) development of prediction models for the likelihood of
occurrence of each event type at different temporal and spatial resolutions. We
analyze the association of event types with socioeconomic and demographic data
at the neighborhood level, identify a set of predictors for each event type,
and develop predictive models with negative binomial regression. We conduct
evaluations at neighborhood and fire station service area levels. Our results
show that the models perform well for most of the event types with acceptable
prediction errors for weekly and monthly periods. The evaluation shows that the
prediction accuracy is consistent at the level of the fire station, so the
predictions can be used in management by fire rescue service departments for
planning resource allocation for these time periods. We also examine the impact
of the COVID-19 pandemic on the occurrence of events and on the accuracy of
event predictor models. Our findings show that COVID-19 had a significant
impact on the performance of the event prediction models.