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Statistical and Machine Learning Models for Predicting Fire and Other Emergency Events by Sharma, Dilli Prasad; Beigi-Mohammadi, Nasim; Geng, Hongxiang; Dixon, Dawn; Madro, Rob; Emmenegger, Phil; Tobar, Carlos; Li, Jeff; Leon-Garcia, Alberto is a scholarly article available to read on EtoBox.

What is Statistical and Machine Learning Models for Predicting Fire and Other Emergency Events about?

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 th

Author

Sharma, Dilli Prasad; Beigi-Mohammadi, Nasim; Geng, Hongxiang; Dixon, Dawn; Madro, Rob; Emmenegger, Phil; Tobar, Carlos; Li, Jeff; Leon-Garcia, Alberto

Published

2024

Language

EN